{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of use of the mlexplorer() function\n",
    "\n",
    "Author: Charles Le Losq\n",
    "\n",
    "**The `rampy.mlexplorer` class is in development. It aims to allow exploring sets of spectroscopic data with using principal component analysis or non-negative matrix factorization for instance. For now the work-in-progress version of this function is made available in this notebook.**\n",
    "\n",
    "It will perform automatic data scaling and provides access to popular data mining algorithms from scikit-learn.\n",
    "\n",
    "Main goal is to provide easy access to PCA, NMF and so on. It is not intended to perform multivariate curve resolution analysis, which is the goal of the PyMCR library that integrates well with Rampy.\n",
    "\n",
    "Any help/feedback is more than welcome!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "np.random.seed(42) # fixing the seed\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import rampy as rp\n",
    "from scipy.stats import norm\n",
    "\n",
    "from sklearn.decomposition import PCA, NMF\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples:40\n",
      "Shape of partial spectra matrix:(2, 60)\n",
      "Shape of concentration matrix:(40, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WlFfDnT+Awf3gnS+g4/dg+G0w+VHYoyCvApbvhBoLfjYZcEE0kMimTRFOPbUtoAMbFhZWttxkT2aacKXRyMga/wPcJCLL0OGVfqDiuGvbeE8ZDM3Ehg1F6J+cXlJ06JBGUTF8sQCwIL8Y8EC3TPjVZLjlcXh2OgzvC7/6LlRHYEsBPPI07KkClxe8CRBQUJkQxFUGSUlt0NqLZJYu3cWYMQeGWjfEmSaOcquUmoE2cNcvu7/eeTZwZtPd8dAYoWEwNBMbNhSzN/pgGEUHevWCtXnQpiPsLgNs+O0tcO4dsGoTTLoAdpTDdX+GkJNTw+0GwpCSDtM+h4F9YcE6m7Q2AebPDwEhIMCiRTuN0GgpvsVPVqOeMhiaiZycQrS6OQCUs3Gjn0CivhayAYFzz4Dbn4FdRXDmYJg6D1Zugdsvhnd/A9tfgm/+AUSgqBrWboYLnC0ZFZFENm+OEAwmABZz5hw0c4AhnpgkTAaDoSlYtiwfbbl2kZBoU14u7C4GRGfnEy+sLwXbguIYLNsKv7sW7rgUEvx7+2mfBh3awPYiwAZvCqCgIhrE7YaUlDZUVOxk4cKSFpnnSc+3PAmTWWkYDM3EmjWlaKEBmR20R+TuYnA7zpGpnaCgFMoUTB4D65+G+6/ZV2DU8pdfAApsP3w0D6wYKMtm9OgAFRVJgCIvT1FVFW6WuRnq0bTeU8cdRmgYDM3Eli1VaNVUFJF0OneGmEA4BlhQFIJ27aBHJjz7U2iTevC+rjgLfG6IuuDLJZAUBDzQtkMiJSUCuIEAK1bkNcfUDPUxUW6PnkZEZ/SKyJvO9QUi0qXetbud8hwRGVuvPEVE3hGRNSKyWkRGxnMOBkNTUFUVpqys9hW0hk2bA1geamUIBOF7Y2BTEdz3PXA1Qt997QWAC5RAZkd9np0TxOMBHYrIx6JFO+M1JcPBMELj6GhkdMYbgGKlVA/gCeBRp20/9CaW/sA44CmnP4A/AzOVUn2AQWjfZYPhuCY3twQtIbz4fEJVlZBfiraLA2cOhzV50KMdXHtu4/p8YJJz4kXvFxTIXmtzwQW1YUpczJy5pYlnYjgsRmgcNXXRGZVSIaA2OmN9LgVeds7fAcaIiDjlbyilapRSucB6YJiIJAHnoNPEopQKKaWMtc9w3LN+fSHa1dZFu/bJAJTb6EgiSXDD1bBsU+NXGQCd2kDXdoAXsjfpsooqOP/8AEp5ABfz5xc18UwMjeJb7D0VT6HRmOiMdXWcnY+lQNoh2nZDb49/yUk48ryIBOMzfIOh6ViyJI9aI7jPm0JaG/QKwQ1de8FfpkPP9geuMpSCH38Db21uuN9bLwJcEKv9JbvB53eWL/jIz48QjcaafD6GQ2BWGkdNY6IzHqzOwcpdwGDgH07CkQrgAFsJgIjcXBtuOD8/bmFYDIZGsWRJIVo9BZs2ByjxOBcyYFAvWJrb8Cpj1i54ej38bBFURA7s96paIePTKWFxw+wvPPocL0oFWLOmIC5zMhwE4z111DQmOmNdHRFxoROIFB2i7TZgm1JqgVP+DlqIHIBS6tnacMMZGRnHOBWD4dhYtWoP4Me2Y1S53URtIAFww9oCvcqYeM6+bZSCh1ZCshvyquFvaw/st1t76JgBeJzVhgfmzrXo39+NNpj4ef/9jXGenWEfzErjqGlMdMZpwGTn/CpgthNoaxpwjeNd1RXoCXyjlNoFbBWR3k6bMeybwcpgOC7ZujUKeElKdkEq+sGSCokeyN4Bv736wFXGnDz4Mh8eOgXGt4PHsmFPA9suJo5Bq6jQf3dsh5EjA9Qa3j/6yBjDmxUjNI6ORkZnfAFIE5H1wB04qial1Cp0fPhsYCZwm1Iq6rT5GfAvEVkOnAo8Eq85GAxNQSQSo6bGBXgRb0A/VNyADWlJ4PfA1Q2Em3toJbTxwY3d4aFBeh/Hn9ccWO+qc9B9Oh5U2JCe7uSFxcvy5cYY3qyYMCJHTyOiM1YDVx+k7cPAww2ULwWGNO1IDYb4oQMVJgIW5aGAFhh+QEFJGCaMgKBv3zZf58Onu+F/TwO/C05vBZdlwp/WwE97Q6pnb93Te4HLgogXcNJ1bNte26GfkpIIkUgMl8vs5W0WTBgRg8FwLLz++npqPadCtgdiIElAGEoiDa8y/rAK0rxwaw/41XK4LQvae6E0DKfPgH/k7K1rWdArk1o7O3jhq3lufD5FrV3jo4+2xXWOhnoI+mtvzHECYoSGwRBn/v3vzeilBVoX5QZlQ5IXfD64sN66Oapg8iKYsQvGtYUR/4U/LYWnsuGpHPAKbKqEe5ZAKLq33cTz0eoOC/DChg1C375aJQZ+3n3XpH9tNr7l6ikjNAyGOBKNKlavLgd8eH2AZes9fkrv6xs/eN+AhDPz4JXtgB/+lQs5ZZCRDL8cBK4USEjWYUOKozCzni/iLRfqPnHrIxaFvn2DuiMCfPmlWWk0G8YQbjAYjpavvy4kEtG+sLbXqx/sKUAIKjlQNXX7MrT6CkhMhUgCvHYGPD4Iss6FgAuthgrAP9fvbZeRAh4X2hhuAQIud1CfEGTjxmIiEbPJr9kwQsNgMBwNn36aT+1Orhr82i/WDdSAJxEuGrq37sOrYGMVWBF4bhCURaG1F85N03s2PtwNW6vBFsCCaTugJLS3fc8O7NWTe2D9+r3G8GhUsXFjRfwnbDDqKYPBcPQsW1aCNoILUbdXCwx0oqXxQyHRMV4/tRbuzQYE/qcbfJAHHoG8ENy+Am5cBvesges6wOP9db2oC97etPde15xHXf8EYNVqF8FgDC1JPCxeXNgscz7pMeopg8FwtGRl5aONGIDXCwGQKETdcNUZuvgf67R3lOUBdxRGtIH3d8Hv+8Cd3eHpzfDiFrivF0wdDD/pCuLYL+rvEr/pQva+vfqhtBh69rSoTTE7a5bZGd4sfMvDiJygss5gOP4pKwuzdWspkAEocHsgAVQI7AS4eBisKoGfLIR+qZAdhkvbwJ2roXcC/LK7VkXZAgOT4NpM3a9L4NRkWFIKy0tgczl0TtBJm1w2RNx7x9CuvZ+lSwESWbjQGMObBbNPw2AwHA0rV+5xzrxYbku7PXmBCIwaCslBeH+7rpEX1auHuSWQH4IXBoHH0gJjSr+9AqOW27pS93D6W709G13boVVUTsjP8opk50oyGzbsjtNMDftg1FMGg+FoWLaslNqNEzGXXz9MnGPSebrO9O3QPREKoqCi0MoLn5+tGNlq/4DQe1kSi3BW+6j+8brhxY3aUA5w6dloDyoFeCFnXe2OPz+VlTG2b9/TUJeGpsQIDYPBcDQsXlxErREar1+vMhy79IVDoKAa5hfA1hAgcHZr+ODsGBfbRZxTXcKsaAil9gqPbbEoP6jZwznVJdwaLWNQMmDrmFTznOj/V4/StwMgCfJ32wSDYWq3IH/yibFrNAvGe8pgMBwpc+ZsojZ8CB5tzyAMvXvpQIUzd+oFQciGFAs+PxPepIoyFIUorqjZw7iaUuZGQ/xvuJLTq4v5MBpiuOUiKxbh6k4x/Qu24XfL9W1O687et9gAqBh06AB6k1+QDz5YjyHOfMtXGifosA2G45tYTLFuXSHQWhd4PNqJKgQTRmp10iMrqQv98eQpUI3imUg1YkWx3WEmx4LMDIeZUKNVSpfYHh5xBwkBg6uLcbeuQfCjXDA7DyrCEHRD23TYVYbeci5guQNAGEhl7txNKKXQWZUNcaHWe+pbillpGAxxYO3aPcRiLsAHImC5tYBwwTkD4EfzYHWZvpzugUkd4Y1INaUoAq4YGWLzhl3GQK/iPrefD71JPOtJoExi5EiIHmLxiV1D/0TdZxR4aIW+95gR1IW6wg/5hWnOhxTy8sqdqLuGuPEtX2kYoWEwxIHXX1+DjvfhA49P+8kqIAGe3QP/3EDd2+g9PQEUT0SqsCTGj8THLMngj5LMMgnxpF3Cj6WQzmon31F5/EAVEbGizItFuDIzVhc25Kl1EI45KWBrPagSoagoAW1M0WFFZs/Obfbv46TCCA2DwXCkfPBBDvp136dD2Tqb+rqNhP/uhNPaAG4tS27uDB/HwmxSMTyuKD+1knCJcKMkMF/aMJkg5+HjXkniBWnFVfgpsMMowNfaSeXnhrIIvJ4LZ/ZD/7L9gBdUWEhMDAF+3O4kPv3UCI24YoTG0SMi40QkR0TWi8hvGrjuFZE3nesLRKRLvWt3O+U5IjK2XvkmEVkhIktFJCue4zcYjoZIJMby5XnUvtnj9UGi3qaR3hncFqyqARRc3FYHIfxTuBJBcZ3lpZ3sdatpLTZTrBT+bKXyC0nkUvEzQfyERNFehPneavokaO0XAg8ug/QkSE1BL3ScUOn+oA34EEnls89yicUO7tJrOHaU3bjjRCRuQkNEbODvwHigHzBRRPrtV+0GoFgp1QN4AnjUadsPnVO8PzAOeMrpr5ZRSqlTlVImg5/huGPu3M1Eo7VhyQGPV6uiXBBOgNQkCAFE4HsdYHkswvxYBNsV5RdW0mH7H4oHEehlW3wRCzOxU4yYE8Qwt1KHTB81lH3sGlWhVMAiFEolP7+SlSvz4jBzA4CyIORr3HEiEs+VxjBgvVJqo1IqBLwBXLpfnUuBl53zd4Axot06LgXeUErVKKVygfVOfwbDcc/rr69C+9f6dP4M2wUCEoSNEcgHiIDHDRPawOPhSkBxme2mixxeZ9FObDphY1lRIkCnjiE6+MDyAwIPL4cLh6N/3R4gCGXltcZwLZSMXSN+KIGIbTXqOBGJ56g7AFvrfd7mlDVYRykVAUqBtMO0VcDHIrJIRG6Ow7gNhqMmFlO8++5qwI+IT6umPIKl4PSBUKr0f2A7AuPbQoUd471oCNuOclcjVhm1DMPDWgnTQSxmEuJ3vdGrDRd8VQBJtWFHAjiRbz2IhIEEMjOTjV0jjigRoi5Xo44TkXgKjYYcwfdXpB6szqHanqmUGoxWe90mIuc0eHORm0UkS0Sy8vPzGztmg+GYmDdvK0VFIcCLUk4+16B+oHfrg3a7VRAVuLIdPBeuIgaMtl30EfehO6/HMPGQLzG+Y7n4NBriykxFnwQQL2DByzshEKQuwC4+8PpCQIDMzNZ88cUmk5QpjkRtu1HHiUg8hcY2oGO9z5nAjoPVEREXkAwUHaqtUqr2bx7wHgdRWymlnlVKDVFKDcnIyDjmyRgMjUF7TSWhX/HR9gw/4IZAG7THTFRrrCa0UTwfrUasGL+2E4/oPsMdf90OtlANfKZCPNJX69Nxw/RtMKgve11vA+D2eAA3JSXJlJWFWLp0V5PM2bAvCiGK3ajjRCSeQmMh0FNEuoqIB23YnrZfnWnAZOf8KmC20sF2pgHXON5VXYGewDciEhSRRAARCQIXACvjOAeD4YhYtSofkSTqUuh5feABtx/yLPQvLgbntYWFdpgipWhjK4bXBYxqHH1wkYhQLBHSEd6P1nBZWzg1CW3HEBg4GL0+12GnqImmA7BzpxY4xhgeHxRCBLtRR2M4nBeqU+e7IpItIqtE5LUmndB+xE2pppSKiMhPgY/Qi/IXlVKrROT3QJZSahrwAjBVRNajVxjXOG1XichbQDY6GMJtSqmoiLQB3nNCILiA15RSM+M1B4PhSFmzphClWgM+sN3gshELzugHWaXUZda7uj08Ha4CFNdb3rqwHjVE+BuLOI8uDKLNQe9jizBEeciSEOPsAB9GQygUTw4QvjNP3z7UCr3K8ANVEFJpQAllZX68XptVq4zQiAcKIdREcUTqeaGej9bALBSRaUqp7Hp1egJ3o1X3xSLSuklufhDiaolRSs0AZuxXdn+982rg6oO0fRh4eL+yjcCgph+pwXDshEJRcnP3AJnYdoCoV7vaKgtGDoQvIoAb3G4Y3jbKL6NhbDvKd62Uuj7eIJsv2MJidvEk55NRq+ZqgGHi4TFVxvctF69Ga1itopyb7qKLDzYpvRfE9kI0gH4l89hIZTWxWIDevdPJzi6I91dyUlKrnmoi6rxQAUSk1gs1u16dm4C/K6WKoU51HzdOTJ8vg+E4ZMOGImIxH+AlGnVp1ZQPcEPbrtQZwSd3gvelGoWity30dQzguZTwHmsZQjsixPgTC4hycGP1cDwoIGhpH5F5Ub07fGJ7QGBlGfTsiH41VIAHPB51ciT8AAAgAElEQVRtDG/dOsWsNOJIE9o0GuOF2gvoJSJfich8ERnXRNNoECM0DIYmIienEB0K3dlV59P2jFYpUORFC40Y3NZd8WKkGsuKMdHWK4koir+ziEQ8/JJh/JjBZFPAK6zkPhbyDgfmwTgNDxaQS5j2YvFVTAuNH3TW16sEzjrFqewBvOByuwGLgoIkNm8upbw8FLfv42TlCG0a6bVens6x/zaCxnihutB23+8AE4HnRSRl/0ZNxYnpKGwwHIfk5BRQF6QQtOeUG24eBzN2Aha088LWQIi8kMJlx7jcETD/ZQNrKeIOhpGIh+/QmaXs5j1yqMbL1+SRjo/v0L7ufoli0V+5WShhzrS8fBkLo5SiV6IgSoep6DEQ+LczpDCESQOq2LpVC6vVq/MZOnT/F1fDsaDVU41+tBYcJrJFY71Q5yulwkCuiOSghcjCxg7iSDArDYOhiVi9ugDw6k19Lg+4LPDDreNhWRkgWjX1YqQaN4rhlovO4qKQKqaygtNow7l0AkChqMFNDCGVGH1J4jGWsYaSfe45HA+L0YmZdqoYuUqrs1q5ABsKfPpv7eInRBoQobRUF2Rnmz1MTY02hHsadTSCxnih/gcYBSAi6Wh1VdxSNBqhYTA0EcuWFaDdo/x1rrZnngKxAIQdJcOETlE+jYWJ2VGusvSD+xmWECXGrQxGHG3EVNYxix1cQE+iRKliD0lY3E8WeVTV3XOYeKhA0dr5Jc9zVFRDUwALZhbopEz4cfKGa1eqSCSA220boREHFDSZy60TKaPWC3U18FatF6qIXOJU+wgoFJFs4DPgTqVUYXxmZ4SGwdBk5ORUAj6UsrU9ww2//R7cvxywwSPwgbsKAdyuKJfgZx1FzGc736Mf7UggRITf8iHvs5D+xOiK8D16YiFUsodqyrmHb6giAuhwIgB5EiEV4SvHGP5DvWBhXSWM7I/WjDuxqGy7GvDRoUM7Vq0yQqPp0eqpxhyNQSk1QynVSynV3fEqRSl1v7NtAaW5QynVTyk1UCn1RhwnZ2waBkNTUFhYSVWVl/qb+sQL406H770LuOC0FMXL0WqCVowzxUtrsfmQbdgI4+nOWkr5C3MpJo8EvFRSwhs6vCE9aM0wejCN9ewknyv5LwodCHEQMaaTykh7AF87K40r2gOLoMaCsSPhvU+docXAdtlEo+BytSE7e0MLfFvfbprY5fa4wwgNg6EJ0J5TfucQ8HgYPgiWlEB5GPBAsFWESsDtinClJKNQfM02etCKR1nOAnaQTgHtSOdhLsSHTSUh5rGBl/iaYXTmj4ziMeZTRBVtSaI/7ficcsqkmGqpYoOy2aVitLUtXAoiNqR2ZJ9NflFnX8iePQnk5ZVQUREiGDyyHemGQ/NtFhpGPWUwNAHac8pHnT1DhD/fAQ+soi4d68pgFe0sSLbgQvxsopRdVLCSMlZQSH8UXmx+y2h8zkMngIcx9GEInXmLRSRi8yzjmcQAiqlgLus43zFYbLT0quSdiLZ5ZPr0vb8ohdatqAuHFXWlAWFKSvSqaM0as8mvKTGxpwwGw2FZujQPnWnJBwFtzxjWF77YRd2vrDwpQpEdYhJBEsXiK7YBEMDHr+lFHoVcwWmkkbBP34JwA2cQwMPTzMECrqYv/2AcI+jATFlPKlGGWoUIigdi5byuKhjjxOmcvgvGjmCvXsGbAtQQCmlDvLFrNC0KoQZvo44TESM0DIYmYP78Auqnd3V7YVGRo5qyFLY7RlufwrIUN4mOV/4Zm4liMZHuvEMW7UhmPP0b7D8JPzdwJpso5D8sAyCdAL9iBJMYQA0hCq09nG5ZuGMWv1QlTOwSAwXbQ3DdaCCG3uTncwE1gA/b9hoPqibGrDQMBsNhWbOmhr3pXf0M6glP5DgXvQqSIlS5w1wsfjqKi63sIZ9KgviAPeymjMmMwHWIB8kQOnM2PXifZaxnbwiQ76C3gLuI0tauoFIJYQXhVjUQhagNvbujhYaTlMmytTtVamoHIzSaGCM0DAbDIYlEYuzZY6OTVrjAtrnluzBtm1NBCcGkCDVWlB+LVj296sSb64nNdFYwjC4MPCCkEMSIsYTP2c0WACYxnFQCTGEmH5NNjBgZBOhLGkGgzNqNAlwxi7nU4HNcbecUQnoydc5dypWq/6p0o56KA00ZGv14wwgNg+EY2bSphLqkFQFtbc44HcrCYCdHACGQEmKYeOgtMf7ELBawCYsou8hnMJ2YzIgD+o0QZgYv8hlvM5NXUMQI4uU+LqQXbXiZ+fyeGWyjmLPoSIgwVdZuXEDrmJs51NDTydz31nY4cyDUbkJWnnRAUV4eJDe3mMrKcPy/qJME1cT7NI43jNAwGI4Rrd5JAFzaCG7Bu7Xao6QoANWpVdwqCcwmhyy2EcNmEO14hmu5nVGk7BcCvYYq/s3fWcsSOtCdQnaygRUAZJDAr7mAH3MOOynlt7xPmEoE8EiEjlaYaExYTYSz22i7xtfFcN35aBWVDQS0MbymJoBSxoOqKTHqqWPgcBmnnMx8bzrXF4hIl3rX7nbKc0Rk7H7tbBFZIiLT4zl+g6ExzJ69Ax3dFvD68Qfhna2AgArZuBPDdAkIF+JjIZsJOzqiWxjcoA2jnBLe5Al2sIHWtCafbGwsvmYGyglwKghn0YP/5Qr60Y5pLKUPaQRRuK1CtitFLCa066DtGsVRuGgEEEabXnwW2hjuB1zGrtGEaO8pT6OOE5G4CY16GafGA/2AiSLSb79qNwDFSqkewBPAo07bfujAXP2BccBTTn+1/Bwdh8VgaHHmzHE29gmAm079oCoK+KLEKlzY7au4SYIUSzm5FBDCpi0JtGdvXvBqKlnHUmbxGq/wCEXsxKaaGooZzFgswuSzjdz9shsn4edahhImShouQkRIcm/BC1gRmx3J1RDVEW+zKyHghEjHArEEra9KMLk1mhCjnjp66jJOKaVCQG3GqfpcCrzsnL8DjBGd9/JS4A2lVI1SKhdY7/SHiGQCFwLPx3HshiZGKUXlrl0tPYy4sHFjCL2pzwsi2H3AbYGVEAMUye0ruZYA/8t8YgggjHY8nqqp5B3+yj+4iw94jtV8Q5hyPEQZxoVMYgpncDXncT2gmMWrdauNWjrRip60Zge7ESAgIc5wVVATs5ktNQRsXf+FXOjdEWq3ByhXEgDBYAeTxa+JMeqpo6MxGafq6jjRHEuBtMO0fRL4NRwipZnhuGPhgw/ySocO7Pjii5YeSpNTWuoGvJCgPaPWJEFYgYpa2ClhLgm4eEVWs4HdRAhgIZzlpEhYxKdsYQ1DOY/TOQeLCjLpyGQeYTiX4XHcePtyFt3oSwXlfMorB4xhDL3Jp4xuJONHYbs3EAB2RoTuSQoUzNgNo093GgjgTwMgHE4x6qkmxNg0jp7GZJw6WJ0Gy0XkIiBPKbXosDcXubk2G1Z+vvlBtCRbPvqIRQ89hIrFWPDb36LU/v8NTlz27KkBnCRpCfoBH0sDRGl7Rvsq2li7+IBctBXB4vsMJJNEqqlkCZ/Tg0FYhFjJLLpxKpfyPwQ5MPHahdyMCxcrmE8OX+9zbThdCeLBQ5QQEfZIBde4LGIxm+QuVRCDLTUwchAQQTt7BVOACKFQkPXri6iqMh5UTYERGkdPYzNOdQQQEReQDBQdou2ZwCUisgmt7hotIq82dHOl1LNKqSFKqSEZGRnHPhtDoynZtImFf/0rn997L6UbN/LpddfRasAAznjiCXbNm8fmDz9ssF3lrl0nnED5/PNtaM8pBT4vkgApAZCEGIiiQ7tCvpbNDMBLDR4Gks7l9AJgGXMIUY2iiiV8xABGMY4f4zqIgdSNl+FMQGHxCf9kG2vqrnlwcTY92c5uLAQXEc51lWGj2NimGmIKZUNlOhBCCw2v5XwIAC4n6KLhWDFhRI6exmScmgZMds6vAmYr/dSYBlzjeFd1Racu/EYpdbdSKlMp1cXpb7ZS6vo4zsHgsOHjj1n+r39RXVp6wDWlFDuysvj8vvt4btAg/t61Kx/ffjtfPfwwU0eMoKK8nOq0NGb+7ndEUlOZf/fd7F6+nLcvu4zdy5ax44svmD5+PC+3a8fyJ55ogdkdPe+8sxUIgKUgakFrRzUlgp0WYrC/iERstrIHF3A3ZyIIIapZxGzSaMM2VjCcyziHa7EO85M8jXPx4ieKl+n8jUK2110bTW8UMdrjx02MNVYhQ10We2yFnRgBgU/LwePk1QCcNb0PcLNixe64fEcnGyf6SkNEvj7U9YOa70VkBvATpdSmo7mxUioiIrUZp2zgxdqMU0CWk0DkBWCqiKxHrzCucdquEpG3gGz0Yvo2pVT0aMZhOHYi1dW8edllRKqqENum/dCh9LniClweD+umTWPnwoWEysoACLRpQ7cJE2jTrx8rnnmG0vx8LCC8aBFt+/Rh+8KFlBcXs2noUKKhEBv/+18SQiGCrVuT3LMnSx57jP4/+Qkun69lJ91IFiwoBdqBPwARUB2gIgpELdxtq7GlDIsQlcQYQxsSnKf1cr6kmgqgnC4M4nQurMvadyg8+LiQG5jBS1RSwdv8H9dzHwm0ogMp9KUtuVQAsJR8fuDqxfxoFYHelZQtS+bDPOjbFZbVrvndQQgpLKs1ixbtZNKkQXH5nk42jleB0EgO+eM7lM/XP4GPReRl4DEnafkRoZSaAczYr+z+eufVwNUHafsw8PAh+v4c+PxIx2Q4cpa/+iqxqir9M4hG2TV/PrvmzwccA5Rtk9ilC97kZEq3bCF3xgyKZszARr/QhgApK6Ny4UISgHKAUEifh0KUu93sqayE/HxSlCLn5Zfpf8stLTLXI2XLFhdgQWKyLsgEPDGwhMy21RRLKR7C+KhmND0ACBMii0/w48eminO4rlECo5Yu9GUy9zKd59nOBl7mISZyF61oy2h68ze+wCKRMioZKAqXHSGcLoCiICyMGgDLtqD/cYKtIFSIy5XBokU7m/jbOTlRyAkbIsThkDrig66FlVJvAacBSUCWiPxKRO6oPZp4kIbjmHlTpiDADUuWcNOKFVzw+OMELYsg0GfkSG7fvJm+I0cSW7aMxOJiUgHb7abD2LH8tLiYzJEjqXK76XbrrVT5fPjT0/EAkaQkgh07EgmHSe3YkfajRhEGsn7/e2KRSIvOubFUVzthzJP1ju5gJuAWXK1rGO2twUWEIDbJWPSlHQArmUclZUQoZTiXk0irQ9+DtUQp26csSBLf5ZecxjnUUMOrTKGMYobShSR8JOPCRYQdUkIP20IExBPTr4ldqQ1yCwnJgCIUSmTRop1Eo8Yp8Vg52fdphIEKtGd34n6H4SRg5+LFlG7cSPqAAbQ99VSS2rVjzRNPkN6pE6OffJLipUt5s18/1r/+Ol0vvxzL46HVwIF8f9s2Lp85E39KCt+dPp2Url3JevppUnv04MYVK+g6ciQZiYlMnjWLM++9l8LVq+lx3nlY7dpRuWMH2S+91NJTPyxbt5ahjeBh8LrAgupWgBJcbWvoLIVYKGxqOI2OuLCIEOYbPsZGaENnBjL6oP3HqGInD7GBCWzmByj2XewLwii+xykMJ0KYd/kbijDn0pNqyhFgLts4X7wICis5DBZsCaId1l2Ay9bjJ0BVVcwYw5uI49GmISI9ROTMBsrPFpHu9YsO1c9BhYaIjAOWol0rBiulHlBK/a72ONqBG04sZv7kJyjg7PvvR8VifDppEpW7d3PB229zys9/zvjp04mUlwOw+YMPaDVgAJd+9hmB1q3r+vC3asXEmTMZ+etfc/3nn5PYti0j/vhHKnft4o0+fdj03HOkZmby+T330Gb4cKqBL++4g1j0+DZjvfDCWsAPdkw/d1Mh6lLgitExI0yOpTczxqhiiLOZbxlzqaAUmwijmXxQw3cli9nAxRTxMgmMoopl5PGXBusOZQJuwhSxmw95kbH0we9sI1xDPmeLF7FjeNqGQGBpBG1lrH1mWUrPAw9ZWfs7OBqOFIUQwtOoo5l5EvZbsmqqnGu1TDpUJ4daadwDXK2U+o1SqvLIx2c40dm+YAE7FizA9vtJbtuWr+64gy3//S9nPvEErYcMQcViLJkyBcvno88NN9Bx/Hgu+fRTfGlpB/SV0rUrox99lIBzrf055zAxJ4dzn3mGzDFjCEQieJRi3bRpVAKF5eX8o3t3ClYfv9Fipk3LB9zg9Wuh0U1/dHeo5iKXTTEVBBD8uDmFDpRTyld8gBBjMOeRvo9XuSZCGasYxgauIUaITrxMMheQyPkU8DTlHOjYkkwGmfQkER+5rGIJMxhDb2zChKihOxFsK4aVqlcqNQJdh6A11y7AGwA8WFaKERpNQK1N4zgMjd5FKbX8gPEqlQV0qfd55f516nMom8bZSqlVxzJCw4lBNBzmw9tuY9eyZXVlSik+/fWvAUhISmLaOeew4s9/JrFrVzpfdBEAq/7xD7Z98glnPv44o55/ngnTpuFNOXBTGoAKhYj99wNii75BFRaglCK5e3f63Xwz5/3rX0zesYOr3nuPNLebNpmZeG2bks2befvyy4/bvRs5Oc7PJ6kVREGc6ObuDlWcYucDCkUFY+mLDzdzeI8IIRLxMZSLG+xzN38mSikKwaI1RfyVXdxJDQtx05Ht/A8Rig5o14czCFFCH05nGXPoRik+ogjwhWxioGUjHqVXRTaknYa2a3iBgDbiu92tjTG8CTiObRqH8oryN7YTExrdwPqZM8l66ineuuKKun0Yq155ha1z5mgthrOj3tuqFRXbt/N67958+fOf8/Wdd9Jx3Dj63XzzYe8Re+R+ohMvITpmOJHuGUQ6pxA5fyRqnU5vJyJ0vewyLv7oIygtJTUxkSBQlJPD5/feG6+pHxOVlQFAQVoy2KA6gZUcITWoWGxtARQ+YDwD2MY61rAQmwjDuAh3Axu7FDGKeRvBRQY3UsUKSllMEtcRowI3PqIUsZ27Dog/1Z0huPDgw6Y7A8liBmPpBii+ZitjxIsQw0qKgA15KXroWIBPmygjkUSWLNlJJGKM4cfK8WjTABaKyE37F4rIDcBho2zUYoSGgZWvv44nIYHSzZv59+WXM+PKK5n+gx9goV3nMgYNYviUKVyTnc21a9fS/aqrWPGXv2D7fIx64QV0jMmDo7JXEvvbn5CrJmK/9j7WI09gXTMZtS6H6O03omJ7H1Ltzz2XSz/7DLFtPIAlwlePPMKiZ5+N63dwpOTm7kHnBK/RYcZPBdzg6VrJYDvCJopxE+Z8+hDEzWzewsYmmUT6cU6DfRbwEjGq8ZJIKX/HTwYu0inkQ5L5ETVkE+AUyvmMIvYNhODBR3dOZwNZ9GEoUSKMJBU3EQooZ4TYiK1wZ4S0Mbw29SuA2w3EiEb9VFVFTW6NY+Q43tz3C+CHIvK5iPzJOb4AbkRHDm8UJ6bPl6HJCFVUsPY//6FDly7UbN1KwWefsQn9Eup2uWh39dVc/tpr+7QZM3Uqp951F2JZBNu3P2T/KhYj+stbICkZ+9G/IGnpdddk4KlEf3YD6tWXkO/fUFeecfrpXP7ll/x75Ej8JSVUAB/ecgsVeXmcfc89hxVSzcHbb28EfCBV2p5xDiAKOy3MKXYhywE3ES5kIEuZQwE7cBFmCFfgwl3Xj0JRzWwqeI9CpgMe3GzCIkg7piIEyeUmCnmHVCayh9fx0oNCnqcV1++zv6MPZ5DD14T1ThhK2U4nUthABbvJxWOlE05xPLBckNofihcBloAVg5gfcJOVtYMBA/Y6MhiOjON1n4ZSajdwhoiMAgY4xR8qpWYfST9mpXGS882jj5JYVUXFunWo1FTK0N6YAyZPpioS4ZQf/rDBdmkDBtCq3/7pUQ5ETX0BtWAe9gNTEM++7yhy/Q+RkWcTvf9OVP6++RxS+/Rh9Msv69UG4E9I4LP77mP2Pfcc3USbmDfe2A24wOOF1kBnsFNCiCgKrC1YROlKazyEmcd0fHhIIpm+nFXXh0JRzH3kcy3lvE0Yt+NP4yJGFds5k51cQIByPOymmk/wkoFiPRG2UMWSfcbUgd4k0IpcFpNCBjvI5RpORYjxsazndMuFlRAFUeCC1v3Q/9geBW6daMPtTmTRImMMPxa095S3UUeLjE+pz5RSf3WOIxIYYITGSUskFOLru+5ixUMPgWVBx47kb91KYqtW+Nq0YenLL+NPS6PrqFENtldKof6fvfsOs6Ou2wb+mVO2Z2s2vVeSEGKAUEJvAQTpHRQVRHgogqAoggaQ/gCigqAgiCKhIwSCUkJvCSUJCSGE9J5sdje72T1nT5n3j1lKICggPK/g3tc1154zM78yvzM73/nW+4MhsSvm8+o/3v/+8uPCU/YUXH2SxJCk2G9PYLdqfnoYb0cBHEEQiF99Peub5c496yNj9N9vP/33318x2pqb9d1hB89ecom3J036PJfiM+HNN9vf8KvL2RqZUEH/Fn3izZYFze2mqeGmeFRGWk6zrXxDTKjJ0/Iy6p2r2R9kxLWoFggVyiiwiXLHtusQeXGdFegstAzLJbQpkrbWHzaYUyBmE2MtNkutnpabZ3NdBQKNMraNZQSxUFCSI06++/stFUZaRnFxjalTO5zh/w7+g81Tnws6hMZ/IVa8+KIbS0u9fvnlWrAun7d2/nw9Bg924oIFDr3zTkEsZvghh4glEoQhp+3NPr05fR/hed+W27aP7PYjhIsX0bKOc/fgvD350zn8+X85dRyvPivM5NnlIL5/AcecxQuPcNQofnQQb70m2GS42Gk/Et7xZ/mnPvrSs+PvfqekpEQcddOmqd10U/cfe6ym5f9/H2ypVHuG3OCaqG7CCuLVOcMSyxHKSBil0gzPK1KoUmdDjbXaH833PfPsrcmNMhJitpKTUSIloYuublPjErWuxSpxCb09J2YfzaqUi9Kk2jwgZcMAx6G2FbY/tlo0Wa9eN1UIxC0Wj+ckqtuIhRa8S0KQ954zPJ3u5PXXV3Q4w/9NdAiNDnyl8OTxxwuzWZVjxlgv8l+MPOII35w2TUGnTvruuKPvTZ1q90svjRq89CgvPCLsOUA4YyqT/iSRWyKReUu431Dhufuycj5j9uHOS7jlR8I+m8q+2SI86lzBZRM4/jxOu5wHFvK9XzD1CY7ZnGcfEjvrXPoNkDvzJOG6dRvMtbR7d9tffbVipNat03vkSG3Nze775jctf/55a2fN+r9ePjNnrhU5wVvZvIQkQSIqe5KIrRKXU6XWfFNkpGWtM8a+SFntJjEFWizWokJGuZR5SrQKFOrmdgldo2t3oGqXS3nMclspRSChzgOS9kJouf1lzH9vbpW66m6QOgvAMvOM1QehhVYqioXilVmCQCZGca/2hgWRqSSdLpFKZTtImf4N/AfnaXwu6BAa/2VY9eqr6mfOVD5woPWipOABe+3lG7fdJlH8fqh299GjFb2bc3HzxcKqLrJvrJV9ZZXclt8UXv80e31LUJoSvPWMsKA77ywXNqOIcPl0wTbbiJ15zoYTKK/ihPE8uJD+w7jqdEEiLn7ldcydI9u3UmbUANnD95X7xdnCuXMMO/54vceOlcCM22/XtbbW/Mcfd9t227lv7FhNCxf+3yxeO66+elZ0kbE2hmEeyU3TCqS1BBmBwAg1XjVZUkKNrobYxmo3yWlUok5Cm4xAVpsCjQKhGldLGrDBWGW+pbObFdpKm2cUWS1ljiZTtCoSarHYrlZ6v6T8JrbTZI2EAsvNs4++YkKrgpStYhGbIJFfo3ZzUeHCokS0TwniHUl+/wb+g/M0Phd0CI3/Mjx94olg+OmnWzFlisKKCvv/5S+C2MZvhfDlJ3j1Kfm5a6hbI377AxLX3yrYYgfBST+jtljYlmTeUuFb0+Tmkl1dLaiKi1e+LThxKEd35dAKju3NnClRx2UV/OBKFs/lrmvFdttT/IEnxH56vmDLrYWLF8lfd7XsuLFMf90uN92kLJEQom7RIjUFBVqDQFkm5bGjjvo/LXA4aVI94tQm6YbZJDdJG5mMGIrTEnpaqVmDUKttHaLZE1b5nbishOKIbVVpu4aRkbeJcvttdLwSX9fZH/Q0Ux8Pq7QNcgikFCKtyZUaRD6lQcYoUKhIoWXmq1FkqBqhQI/YArHCXOTXSJAbKDJRJWLEAxQqLOxwhv87+A8uI/K5oENo/BehfvZsq6dMUdytmxevvRbsfcMN75X2+DDyLzwrPHk/YZZwjyMlXpwptnd7JnMmzWVHCAqL+d0rcrXbyC4tEPzgAvEnFwsumSzYfE8225ltD2SP7xCLc/43WNWuGWy3N9vsyY0X0LBGbMddxH98nsRNt0s+P13i5TcpLZPdbxcVjWvt99BDtj7lFFm0tbWpCUPLWtNWPf+8qRde+MUvYDuWL2+Petm6nRFyXSgoDnVJrEQorUCLqWLopb8Cd1nofxAq1iqnXAESVojpbr1SlY7b+GAfQCCmwGb6+JNhZujme4qlxOTF5a3wQxmrFSgy2NbS1lljqTYpJ/gaQissEo9nJWojv8aawvc6fy+CqrS0qsMZ/m+gwzzVga8MnjrpJFC7++7Wzp6ttEcPIw4/HOSffUr+1hvlrrlcbvxPZL9zuNz+O4hZL9znWInf/UlQVvp+Z7f8lHde4/SbBYNGik96VuLNFeI/Pk9QUsKmO/Cj2/jhnzjlek74Fec/TCbF+H1Y384AePqVtDTx+/EfmW/Qf6DEpGeo7Sp30Dg9CxN2+58TjexSpRXxwkIFIeuSCS9feKFlTz31Ba8gixevF4adkGO7zqwiNjwrIaNNSpGYHpo0Wi4mY7i4enegSJHu4vJCK8XkVbtAHy8ZYIIaR36qeQTiqvxQWg+hbggl1VviJ0Kh4XZARii0wgIDdVYoyjrvHl8tXpMhCKRjBN21Z4aXIqm1tdi0aStkMv/ZBSP/k9FhnvqMCIJgryAI3gqCYG4QBD/ZyPHCIAjuaD/+UhAE/T5w7Kft+98KgmDP9n1FQRC8HATBtCAIZgZB0FFt938l768AACAASURBVBMg3dRk8rnnWvLkk/LJpGkTJshji5NPBrnLx3PizsLx35Mff7b8tVcJn3tKfMxgYWm52FHf4czenNaZG47mtnO4/2q+cSrDt+X1iYIHzhdMupBZT5D9GL6uPsP52b0seYuLD4nOGziCA07g3uuZ91GndtCrt8TDT9O3v9xhX5fdZUu7lyV07ttHc3FxVEwjk9WI28aNM2HvveUyn5ov7BPjj3+ci1JirQwqZCbJcSn94gvlg5gWMaMsRmiIzSU8KaGXUJu4d+TExJTo5QkVjheTUGoLwWf4V4wp0MmeUjJRiRChtMetNUEX/dSIEi+XtTvKB6gQBjHd44slK9uifI04JduL0nwLotJEqVSJdDpn5swOZ/hnQUfI7WdEEARxXIu9MRxHBkHw4Wyw41AfhuEgXI3L2tsOF1G/jsBeuK69vzR2DcNwlKhww15B8G6ZuA58HF665hovXnSROJqDQJjL6T9unM2PO07uxuu4/nyxYuI1JHYcIPHgRMl/PCW2eq5g3F78eh/iScYcxqsPcfslEdH03Af5QReu+QYPXsRjv+aK3TitluuP4uk/8toDzH6KRa+zfDbFBex3Iq8/xo+34Z7xjBpKVSFXfZ9s20fmH3TtJjHxScHmWwl22l3x89Md9NDDsum0RhEzoDCUbmvzziOPeP7dqK8vABMmLEAh3QISAWtCiaFp/ZKRDyCmTWCxhNCWxkiZI6NOXFxCmwBVfiJp4D8b5hOjwjfkpQXt2kaBnGUu0WaBkXYSyFvQHpa7fXtV3TI5Fcl6iaoM8VCmb3tnxZGNPQxLEHQ4wz8jvupC44vUj7bC3DAM50EQBBOwv4j3+13sj/Htn+/Gb4OoRsT+mBCGYRrz2znEtwrD8AXtbKFItm//mSVQ/0MQ5vNe+f3vlSFfVCSbStlh/Hg7/uIX8n/9k9yPT5YYlBDusr/g4JMEl53EqXvSpSedE7xzD71GcvpDlHVm+gwKZrLzPsRDdvsfBmxNvy2iAWc+GgmKaRN56faPn1gJ3no12oheXxY8y+FF1HSjz2gG78C446iqFVTXSEx6+r3mtV27OeS++8y48UaL7r7bVt2rPdGYkm9p8cz48TY56CC1I0Z8vmsZht5+OxdNdvOqiIWgc15ZQbNYrFVOgVGWIbSZ7YReEgqE2hRqlZeU0Fu5b31ucyq1lYRaeZViVsjLKZSy2E8M9keT3W2lRUJ5uxjgRjNUCtTG11jVuQdrC7Ql0R2Zd/+dihUVlZk6dZnjj9/8c5vrfxO+rP6KT4IvUmj0xOIPfF8iyp3d6DlhGGaDIGhETfv+Fz/UtifvaTCvYBCuDcPwpY0NHgTBCTgB+vTp8+9ey5cW8ydP1rJ4sSqkiotV9+5t7Nlny//tbrlTvis+qp+geQHfG8+gTfnrdP50KQ9cSEWeIbtx2n0Ud+IPP+TtKZxzN9sdvPEBNz8g2vI5Vs+ntZGWxuhvWytlNVR0pVMX3pnGsrepW8Lapbz6BA0rWLqc1cuZ/TC3n8cOJ3P0mXTbkH9i4J576r/bbm57+inLVq127GWXuuWn58lnMu7cZx8nzZ0bJSd+Xms5v0UuVx592bUrb1KwS5veicVyYUI+CFSaLylmGwdaZD+hmLiYhIxQoNo5gg9GzeRbWXYp6QXkmsg1k19PspbCgRQNpHAQ5bsQS35kTpFv4wir/VqpQuQktWr2Kpbrpp8lFlplsa76qpZQL6081ihZndamLIq7HollQcTkly1UVFRtypQOTeOzIC/2uZYIaSfEu0b0S90YhuFGVekgCA7BXRjTzpHxheCL9GlsrKrch7WCjzvnY9uGYZgLw/Br6IWtgiDYdCPnCsPw92EYbhmG4Za1tbWfYtpfLTx30UWKkAoCrfX19rruOrFZM+SOP0qwxRaCfB27HxoJDCgsouF1wjyNePE5LjmMG89634/xcQLjgwgC4k20zaLpUVbfzPJf0fQIwSo6VbPl3ux3Gt+5nDNv47bl3LaSI84jWUk9giwP/oYDB3LhcSydv8EwsUTCqHN+ZmWO8M9/dMyjjwqDQMPChSa1hxd/XnjxxTpUEMsxoJCFJLdq0Tu+RD6IKdcskDfaLkKLpNp9G5VGCFFghBL7btjpkvNZegFNT5N6m3wzsaLo88rrWHAyb+3JvG9/7Lxq/Y8SY2TExWQRKJS2zmNG2QlM8yTYVK18QEUQU96pIeINj4diw0V6e0ERCqTThaZPXymV+nJwtf+n4fMyT31CM78gCDrhNGz0JfrzxBcpNJawATVZL3z41eW9c4IgSKACaz9J2zAMG/CkyOfRgY2gadky8ydPlhQJjRFHHqnfTjvJnXIctV3E99lR0NLEcT9/v9GNJ/LU36jtzi8msvf3WTGP+65k8JYcd8U/HzQMWfAAd2/GfZsz+VtMv5J180iUMOt3PLwnt1YxcTue+j4vncO0K5nzF0pL+OYF3LqEI8+LHBaVcXbYg7//laNH8dhdGww57PjjFZaWmj5zjt75Nofec48Qr990k7kTJ35u63n//YtQFPkz8tG11laskohFjvf+5klKGOsAS0RrWqBEzgwEavxyg6q01r/G8v+l9ji+Np/NZjDieYY9zmYzGbOe0Uvoehp1f2X96xudVyCpl2vk1LR/L5SQ1egRg40RCMwXkbHt2s6xUSGrNrFGvKaNJPkCVKMgon1tbU3KZvOmTVvxua3ffws+Z5/Ge2b+MAzb8K6Z/8O4EJcj9fldycbxRQqNKRgcBEH/IAgKRI7tBz50zgM4tv3zIXgijGjaHsAR7dFV/TEYLwdBUBsEQSUEQVCM3TH7C7yGLzUeOe64yPmNwvJyu191lfxv/pdZM8TPv1Rw/+831DLuPo/7b6CkjGumsdU+Uajs79/i5oVc/ATJf6J2L53M/WN5bH9ic+mMrkn6D6HfCPptzrCd6dmZqhSZ51n2F6ZdzotnMfmbTNyDtnVR+OcxF/CtcwgzzHyYbxxIrz6MP4xTx3LN93jsT5KlpUaceqqFWdZe+HObHHigXS++WIg7999fprX1c1nPJ59cg0JGlTM/EN8jpVdyiVwY+QKq1RllJ+s9r9V0MVmVtpWzToFRij5onQ2zzDueZGf6fIwgDmIU9KTX+cQrWXLex84tqatefi0rgXUCZEyXs1q1Llo0W6fOprpLyktLq0rUR3WoBNGToJv3nOFRmRQdJqrPgJDPM09jY2b+nh88IQiC0egdhuHn94b0T/CFCY0wDLM4BX/Hm7gzDMOZQRBcEATBu6mvN6Gm3dH9Q/ykve1M3Clymj+Ck8MwzIncdZODIJguEkqP/l8t1JcNa2fPNueRRxD9yMdMnqx0fZP8ZecL9jtYbM1brP+AljHpKv78S+IJrnieig+Z9Lr0oaTThvvCPKteZsp53P01Ju5K20xqYhRVMOJyBp1BaX8apjL/OlLL6LZXdKz7gRSlOOARvt3AbrezegoP701bUzTGIeez6QhqynlmAvUzI3103gv8449c/W1e+JuRZ5whnkiY9uyLco9OMvanP9V3xx2F+bwHjznm317Ptra81XVR9SdblTM3VLxbsx7x5bLiyjSJCYwyzHInKdKiRFqrvyFQ68oNO1zxK1pepe9vSVT988ETlfQ4m4aJND3/saeVGavYIe26TFxC1jqP62MToZh5XpcU10uJ1iAnKaNT52idg3goPgBl7wqNEiUlJR1C4zPhU5UR6RwEwdQPbB+mwfynZv4gCGKiyNMzv7jr2RBfaHZJGIYP4+EP7fv5Bz6ncOjHtL0IF31o33RRTdEO/BOEYejOceMiO3oyaeBWW+kyapTcfrtRWCT+s/F8f+z7Wsabk/nDmWTxi/voN/LjO8/nWDaZN86meUaUa5FHcU96dadtOb2PYeSvKNx4pjlomUFRHS19mXIUu77GoCOIJXjsCCbtw9cfJlnGMb/h8l056EQ23YfScu4dz9zJkd/jksOUXPKUYSec4I3rrrP+0ENt9/TzDps40RXl5Wb/7W/CMPy3yJtmzGgkLIu+DCniHbpVrhQLckKBblYaZFNrHS5prTziugktU+MKBdrN0LkU8y5n7YUE/Zj7V968ntJBVI6mYjQVI4l/iLK566mRoFn8M4Y9EfmMNoLurrbQPchKiGv0qM1c7DVPmW2Kr9nNtnqZ7x3lYipL6tSVdZUPY4IBAWXvOtsLxeNVpkxZ+pnX7L8V75qnPiHWhGG45T85/q9M9Z1EhEpPtt/f3fBAEAT7fVHO8I6M8K8g/nHCCeoWLxYEgbJMxuDDDhP+5WbhM5PFz79M8MfxpFNRxFQuyxX7Rxkww9B8J81LNuwwl45MT8+ewl968PQetL4aPdDLe1PZjdiqSEvZdiJb/vnjBUbrm7x9ODM2Y/lllC6gtJ6XDyCfYcAh7HobK59j0r7MnUD9A3Sv5rnrefYnXPsNFj7D5gdSiVwbZ441tl/cdt8+xurG9e4aPdqUn/1M5yFDhLmcZ8eP/7fWdOLE5YSFdI7REhfbLK1HYqlsGD1kq9Qb5jmskVYoq1TMWqXG6eSb73e04EaW/4J8lrp1NL1Ftomld/D6iTy1NQ+URdvESh7qzKQeLLyFHj+j6UnWPf6x8wzEdHKKQF4gr9VzKpUqUmSVSABso6+YnAIZXeIrxavbCALZBAoDkgkUamkpMnv2Gk1N6X9r7f7bEApE5Sv/9fYJ8E/N/GEYNoZh2DkMw35hGPYTRZ1+YQKDDrrXrxzSjY1ev/lmIfpstpn106bpt802ct/cQ7Dt9oLOBTxxD6dexoDh/P4YVjbRo4pDTuCNXzH/rihPoqCGphbqnqc2RTxG9wpaMPCnDP8l619izZ+ou4OwgYKP8cOlF0VvyXV/JVZMj3OoPZ5VN7D8KsKpvLY9ox5l0OGEucjHsfwp4kWRiWplAzNmMmRrvn0L3Tfh2kN4aSJ1afFJvzEy2dWA3bY09eUZ3rj2WsmKCiFeuOIK248f/5m1jXvuW4aebFrKnLzKk5p1i6/QGhRLajNcnZjH5ZUJpZUoQ70qF0VhtY1P0PAgq/9IIfpdR9eT3h8gDGlZQMNrrJseCZIwGwmXhleYfgY7vUBBHxafQ/luUbuWV1l7D8medIsy/KudrdEdWCmuTb179THEHNMtNEsfmygWWh9kFAVppVVpDYtKooDOCiSLyBTI5SKB+Mory+28c7/PtG7/jXi3yu3n0leUivCumT+OP75r5sfUMAw/7Cf+wtEhNL5ieOyEE2RzOXkkMxmdR41Ses7B4oPWCUdXCK48jdE7RnkPi6Yx8bboLrj4ZWoHMeJEnjmc5heiDksChgYEScIYQT3lCCcx4x5ScyIhUHVw9PntQ+h2Jr0vifIK8pnIrLJ0fOQD6f5Duv84ykOAPpdGD8/p48i+zCtdqD6OAVdQMz3ScqpHRhnpIyfx5Hfp2kzXQVH7g37JK/cxYgwzpwjSjUo7VdgukTbs20d75OHHlQaBltZWs267zYjP6N948504EgwrpimnR495UkFUdmMTs4z0FAIZWXF5LFPh5xINb/D2oVHuRayEVIbOR9HlQ+HAQRD5fppX8soP6LEL215JcS3pNTw2PNJEhp/LghN45yiaXyL9bghyjKp9KewrEKj2Y3XOlJCx2kVG+p05pnnVE/oabrAKr1svHuaUdl2jIV5BIaV9Y9ZPKaAlKZJugSlTlnYIjU+JzzPb+1+Z+T+0f+fPbeCPQYd56iuEhjlzvHHXXUL02HxzDbNm6ZdqFAuWCEsqBa89RFErZ/0qong9d4eowvZ3zo4EBhRVYSEVg+g7kMqQdMjKDMvTVP2EXhcTL48ie/rfxOgVwkF/lBt+H11PZsWVzN41Miu9sTmLfxy9GY96M4oUSn7IyV7Yly1mkDyEtixrr+Plchb/hE7dI4EBI/dm3+siZ/isG6J93Tdhh++y9nW6lFKcEix/W3xMX9V/m2CfP9ygpLBQgH/84AefaV3r69Oyre1zGFKo+wGL9StYSMgoc33DQ5JaZRUKhQoFkjbRKfwuC0+noBdDH6HkVOpjDLxi4z6JxX9n4m6RlvXO7dw5jDm3RhrfqN9GwQQNaygeztq7KRpK+XdYJRLIyy57r6tyBwiViQtlhQInSWKxuXJyttBDIK8MtcnVUdXbeCDfF6UFIv9riYKCig5n+KfEV72MSIfQ+Arh0eOOkw1DiZISXzs4SsAbmlsgKCDY8uAoWa8gxqUHc/Ge1DUxqAcHfCDB9M3x5FZSthjrGXQHYxYy6FzG3MGQS+j5U4Y/FTlku3xXmOhkpeMsie0u1+8XDPwL619lzv7k1jHkbwx9gMJ+7w2Tl1bvDjntUVKxAja/i23XUfNTglJSE3llIM1vvj+/fgfQY1emnkeqLtq3z1nRg7Z8fTsNYU+xtQvFauMq/3KjfR95RBFa1q711r33fup1vfOeFeSSFAQGjHnHlnu+Ii5nQPCOzT2t1mp5CXkxBfICadWuEKy5M9K+el9MxR4smUCX3Snu8dFB5k7gkW9QMZiDX+Xg16gYwuRjeWgPyrem+wHMPp8+t7L5agoPYPbNFI+ISpqs/iOZaE1iipU5CCTkZLToIy8rY6FZhukuISsnozpWr7RLG2GgtQvK3g2rLpTNdgiNT4tQIJePf6Lty4gOofEVwYIHHzTn2WeFOOjS8ZZfdaHKQjoNSVJQzt9uYdu9OeXXNK/l+UcpCvjlK+930jiD+b+mSzmJGka+Ts1hlPRm+IX0OmyjY7d4RKvH5DVY62I6H82IKfS9hs1mURVFWGctk9cilLXCGdY4y2InCn0g6zhRwqCL2bqBLj8h3sr0r7GuPdE1CNjuGtoamfoLsq28dBLd8qwLGNKb5UsYd6B4WRvPPah7Y53uO+0kwMSjj5ZNfbr8pz/cVk9QomzP0LBtZ2nKlRoWzDLAfMO8JRDKKpLUKimlwtkK86OjTO+S0VQdSN0ztCykz0bqTr1xLY8fRddt+cZTlHSjelP2f5btr4vCmh89mM1+HUVVTfsBC26JzFXd9mXnl8l0IUyz6rr3uq12qrygXWgkDTQPoemeNUBnhfLyQSge5BR1aSIWUhFSnoxyRJTK5wstWNBg9er1n2rN/psR5gPpVOEn2r6M6BAaXwHk0mkPHHusELWbjtDnrissq0vZcmihQIaScoaGrJnE7SdR1BgVDDx+PJ26RZ2EIdNOpiqOFgbfRbLrvxy7zTtWOxUEAs3ukPIKJcPpdhrxKElsrcsstqWFBlmgr7T7JGVkPWmFjZTSCQL6X0LPawjamLUddQ9Gx6o3ZfhJUXb5Q3uw7EkO+x3F5ZR3jSwrbTOFm+8s3i2Q+9EJxl1xOUilUm4dPtyjRx7p3rFj3b7JJupmzPin1/j6tBRhoWE/XyyXids8eFWtOqM1KJBuL0TYIimr3I9U+CFrbiE9j14XRNey6M8kyiJt4YN441qeO4V++/H1v1NY8YE1iDHiJHa6kTWv8PYdUSjz2ueYcQY9DmLreyJB2/0YUgErrokc7yjQU8IAcVkZSaXeFpezwCxZaZuoFkM8zClKrhPv3Ba5MboGlJWJojmLEOuoePspEIaBXDb+ibYvIzqExpcQDXPmWPjww1J1kSli0rHHaqivF2Dz8pjnZq2WR7/RPeg9jC4hPYfxnZvYY88o0+Xc37L3B3xpSyaQfobCTOR36DR2gzHXuE3DB3xxOWvUO9cyOwq1KLW7mCKBAnV+JvQ+gU+zezS6pp3zoRBxoQKhuLi8eje1ExW9j1Co2QRtvcfR52Zyed7en2VXRQJuywsoqGTFc+z4e0adyCGXsHAqFQXMmiM45ngKCsXja1X87io1I0cKsGL+fAsffVS8qEjTggVm/f73H7vWdQ15uca0ml2adduyTllmnR7BchmFqr0mQFwQJco5TaWzyKdZeiGlW1O5D7lWlt5Fj4NJfIDIau4EnjuVvvuxx90kijY+iQGHRudM/TkV29H3u/T7PmMmRGY96HUEzSHZuqjOVzvKfUuAmLycjHIpeXlvecVw3cVkFMiria+R6JJGENVfqC4XPR6qUdRhovo0CHUIjQ785yCXyZi4114e3mcfN3fu7MbqanPuuEMyCCSCwJTnZ5i7lrGH7ii5ej4DBtOwlGNvoGwFTX9n+3MZdfL7na6ezJsnRy+W1YfQbUOHcYOHLXOBRc6wwm/krLfUWOvcKESJfZQ6RdwYpLSZbpnvWuECLZ6x2hkIdXKkhCO0KFThYnHbIVAiZrnx1nv5vTGb/VmdH1jlYNmee9Hv9qgO1eIzmdKb+kcZdy973s+w46NGu57Ejx6npjrSNv5wkuCYH4qVhjx8h+33HicDQaCuvt7YK6/Ud999vXPnnR/LMf7N0xsRM+LKZVoaimxV+ooVQTc7WtheuZaYVkUOU+VnUaNVN9K2mN4XRlrG8gfIrtvQNLX4H1Fdru47sPuEKKnx4xAE7HBdFI32zAmMvpHR129Y9bZyS5IDyFew/MooXBcVvikUk5TTqtgQjRLiZnnJUF0lZIVBXnXQqLq2nZhpACqL2jnDKwVBZYfQ+BQIw0A2E/9E25cRHULjS4Y5t96qaf582/7v/6rdYQfN9fUSyIeh7onQHv059v4/GdUzRW1v5kxi7DexkCk/Y9DR0Vs6NM3mhf14flc6NUZRTP1vIgiEQlnLrHO3FX6oRLFOelnjGnPtLK9RVkwUrzvYAsdo9Ho7fxxpj1rrFiscLpRTYICEPdW7S0w3S12gwetCNUItChRa7GRtlmr1ojo/lReX0WC1bwl77s/wZwjGkFvKgiN46wAa/sHCm2mcHoX3Dt+VS9+ib5fI0T/rVmHnLuJ9Sw2++xZbfuc7cmEom8+7fZdd9DvgAK2rVlk6efJH1joM+fsjjXp/O6didKv5z3cWD/JKwvVKzZB4jzFvE138JmqUb2XZRXTagfLdo32LbqW4F513jr6vfIl/HETVcPZ8gETxR8b+CEp7svUVUTb+7Js+ejwI6HUUDesis9jayOEfU6TApuLahAKV3hFKW26+GnkF7RphIsiIFaQkatoizbQkoFMFSoRhtZdeWioqC9eBf41APpf4RNuXER1C40uEXCbjlV/+UpcxYzSsXWvmM89oKy1V0ruXJA79WrFBO4xR1KWCOS9H9ZqSRWy+BU9+h+47sfNNpJYy7RQe35S6J+nZPTKbDH2IRLm8ZkvtarEt1TlNoXViVuFNpVokLJNVJIY2odVuUGCIYjtp0UWLElDUXnBzvRJ11ljodHlRvkMP5+nsWM3SSIhbI7TOcj+20mFCoVL7I5Q23VpnCzuPZauXI+d68UhKGlh3A69+lydG8WAnntmFNQ9xwZORfX7hEkFtVlC8Xryq3p7N0404OPIrNDU2mnrNNZKdOpl7+0cJo16fRZBfbZML662d2Ul18zItiuweTJUUKTMBurolahBmWXQ2meX0atcyUitZ9Xd6HsHyZ5g6nklfp6QrX39kQx/Gv8Kw46Pf8MWzWL+RN/9eR5IKiXeJwm/bH/LlvimGpKyctBLNYuLe8LT+apSJictJxtaJd22jIKAvyqtE4WhdrV6dsmTJuk8+1/9mhMjGP9n2JUSH0PgSYc6tt2pasEBxeZHFv71YSXGRkupqdUuW2Hdkd8Vhq2CH3SL+i9re1M1kaA+mnk6XrRh7Ea9/n7/3Z/719P0eA8dGIbaD76QkYrqrd7WMtwSGaVEspYesAQocKSo0RUxGTkxakbyEVm9LeVtcQlJWIBQIBPIKJMXkBEKdbKncpprdrVn0NtwiiZxCTXKeEMqqdr4ufqeTY4Vimk3Q7MZoIUqHMWpaVOyvMMeQndjiVgacQutiph7FSzuz+6DoqV63llLiXfMS619xoKdsv9swARZMnaoxl/Pqn//s0TPO8NJVV2lp9xX97t7QwJNXKOqRNeuBIXbZ6mUV4ToVFktqQajUgQr0p20pb+7Gyt/Q5SRKt2XOdTy+ZRQOPPXXPLgzr15I5VC+/o8oSqp+GX86kRVv/+sbIIix0x+ihMd7t4zCcd+6haYF0fHy4ZSPJFUWZYo3PQXKHIxiBaJyIEPMU6OLN71ssHI5rWJCXeMrFNa0IWQIqhPEc9prpneYqD4p8gGpxCfbvoToEBpfArTW13v5uus8dcopMthqxTN26UE8m9a2cpnDqwLDalMM7s99l7LJNnRaH5kYShez1fnUlvPM9pFDdsD/sMdcqktZ9/coNLYyoiVpM8c6N8hjnSXiEmjUwxUyHhETKnGwlK7SanVzlk5GSsopRMw6Pf1BUg8xWeWONshLquwtKS3tBSmviSnVyTiVdpGTFKp+7809r1hpe45BtfMk9BUqtdYvpLRTvgZBVDaj3/U0P0Xqfja9lD3mMHYSVVvT/R2ODdimklrkyKV6UNdgp8I3fX/XAv0ryba0SGezpvz61x4780xPnH02uOvxOgN/mLbsrgr1a4p077fObusmq1m6VlFLWpCnyk+pf4gZo2ieSvJI3pjG3cXMPJnWJeR6M+Qk9vwbx9ZxwPNUDKRuMZfuxJM38Ov9aP0Eb/IVg6N+uo5l0UORBvnX/lFJ+VxbpG2snhcRvi+/CpGJqtyxYkIFUiqtVaheXk6xRWKRp0dF0KRbYShekWWoKHCqpFjEztS3o3jhp0H2E25fQnw5Rd1/EfK5nD+OHatp9mydUF5b4a2GRs+sjCkvKXR471Y1xVBPYz27f5vMdFatZYfN2edqph9G0Rr6bUmXI6nYgeaHo8ztrqfQ7RShUKvZVjpYKK9FqZicIk0CrHaEuJQi39DFr3WVRiimSM6RFjlUm3l6ukGZXSRVajZBlV+KKdbDr+WcJ67yPbrTlBaFiqQdImWFEmsFeklZZaED9HGXpG5qXWO5A8WUW+N7upokaUC0QF2/T5iKMq/f+RYD/0yXcXTqTc+tmXMBQxrIDyZ4W2zpahpDstSUtTlqLOk0C9awsiHvnXW89ZdbbHLZYVOjmQAAIABJREFUb3UaOVeiU2jeb7uo2W2pntmlBr+zQDKTY0k7xWRiG7LLaStiXorE7RQHFCboehgjL6NsI3TDaxZw2S6sX8sRV3Hnj7jxWE6+J8rW/2foPS7awjxrZ7Lgvihn5cWz+NoZzDqH5NeiWletcygeotKp1vmDpKw2oSovK3SMRaZJ2ESRuGyQE4+3SvQolGtMRkQEizvTtAR9PPtsB3XNJ0JEqPGVRYfQ+A/H7Pvvt3b2bPEg0BCGGlY3Wore223jgJpixdNeEo7pLlj1Nt+6iJZ5PP0qmwzksDt4bic6rY4q0ubeZsmZUbFl5Ct2Fet7tYw15vmWnNmKpWQU6GQXzZ4Q2FlSg6zXxXTVud3hG/sAB3JcJ33cJWuNQv1BoTEKjXnvnEAgoct732d41qNut51v2NQ53nG0AqeodaK5dpSx3CKH6uoCRTZT7nvW+b28uJUOUuQ7mj0roatu3X4plm8UW/wLYct0QdviKBOdKCKsGLG3ae4nsFg4eBfZiZPZYZzEzqMUTp5gUHyxoT3ZEeQ0HNvFvhf+3Nx3BqhfWW2fwQ/Yqv4VyUzOmr7l8vFAzax14m3Lo2zsXA0Vq5Gj73EMO5+ibhv/UVfOjUq9p5s54Q7WNXLAhdx7DhMvZr9z3z931hPcdx5bHc4ep23YTxCjZmS0peuZ8atIA6nahrqlUWb3il/R/zpxVYrtrsXfFUrpapUF5slIGaTZQp21hln5eL1EbYn0myGbBEwrFdF4VZsypV4+H4rFPnuJ+f8KdAiNz45/RYgeBEEhbsUWqMPhYRguaD/2Uxwnqo50WhiGfw+CoHf7+d1ExvXfh2F4zRd5Df+/8dTPfx7FKIWhokTCPgPyuu53qJKvHyd34Djxo3cSLHiKgw+g+TWevTt6Qzz8Ep7ZibLVJOIMf4KSzUkvsL7lcq2Zv1rfeb7y4FoNpkmbq0xKRBh0uZUuVWSw3m4WU6LNm+KqxWwY6RMKLTTDVBOtsViVbqr1UK2n7gbpYchHrqnOMo+ZgNBzHtTbmSp8XZ07FRqgxC6aPIJ6i32nnYUmISkQk5OxSpvL5MWlxM3yD3oEOgedVdUtVVhziFjZjpRtS2YVM/eiz3oqVjKpUNC3RPD9n8vfdIFwvxaO6yZ4c5XWv6e9PIMwTmqzbpaM7mvhuZ0FQwv9cr9r1C5cq60grqlLsYL6rHh9F8KV7dwXSyNnd9kRkRD5OIGxfDaX7UpjE11Gc96+EUdJ135sshf3/5y+o+k+jDvO4tX7opLzC15h1D50Gbjxfre+PCKweup4tj2bt35O7W5RkmGvC0nWqDZei78rkJHWSalnldpeYKm0WkFQqCZYaU2ih5bCvHBwnLKAohipmHS6t7lz1xoy5J/wpHQgEhqZ/9+T+OLwhQmNDxCi7yF6t50SBMEDYRjO+sBpx6E+DMNBQRAcgctweDtx+hEYgR54LAiCISL5fWYYhq+2E6m/EgTBox/q8yuDxS+8oG7WLIlYTGVRkaE1oYHlrcI99pS77LtioyvEFjzF16pYej/zRDH2m1bx5g8obKQgQ9/rKN0ChEU91RVNETdWoVoNLpMXaOekU2g/y/1MoaH6uF6sPRKqwLAN5hYJi+mmeNAqC8R1U26sEnWWmWNOO7/9Fr5uawcI2t1nGW3ucJVQ3qa29Iap7vUb33aq9V6y1Pj2EeLaZMTVokUgLdRTRkqoAYG4UKE2BdokbSvffYS3u9+lUo0+77IIFw9h01d5aQSdWjk0xpKHxGrXig2LkXpW+GIgXBFKdI/rlkiYeF/a298aLcyz6E81hh64SO8nVyrtnLK2dxmxmKqy37P/Acz8CevfofN3efQ2XmrP+B53Bof/74ampnde4vzdqG8hH5J7h0N+zJCtuOEHPP843Xvyu8PJZaJCjQdfHGkZv/gafzmFMx7eeLHDeJLd7+CezZnxV2qGs/Kt6JpXXU/PnynQV8IQOW8pEuiszgJLhCqVW6VJb2XBer2DwLratEyqJOIrKa4glUIPU6Ys7RAa/wpRzPlXFl+kpvEeIToEQfAuIfoHH/D7895T4m78NogID/bHhDAM05jfTge7VRiGL2A5hGHYFATBmyK+3K+k0Hjkf/4nqjWaz8u1tNhqz+HEG7nj+xI1GRpQgGX1UYNNR9B1JiXVJNsoTVFz5AZluJvdJ2eZmFCr17UoViQtJhSoVe9RFfbTwy/fExg5Oc+602oLZaWktUhbLyOtXGc7+5Y7NHhenUscq0ah9Rq84mGveFij1XbzXQlJ9/iVlFYjbGGc7yrSyVRPutefHW2ynHo5DRa5wTumqVKvVBaVKh2j1RTrvSwmq9B6xXaz3tOyXhB6QYFCDe5XbleVIue+TkPofzUzT6UgTyVByQuUEGbitG0nV9gik3pDy7X9FO7WYM2xO1k3mdSqQtsWPaZqUZOwM021JWItBUre7spI9P4hD13KHw6MNIJ9zyHVxD+upn4J37s1Cnue9BtuOJ1MnuFjOfxcRo8j3h52uemOXHUsL0+kvJTtD+Coq6lqL2544AXcfkZUBn7LgzZ+w5T2jChzH9qDyp1omUXVUFb+lu5nESvU2cVWOkRSnTZdjdLmaaFellqpt2yQV5Rskegel1lSEjnE59ZQPxeVHn98nqOP3uxzvc+/cugwT31mbIwQfeuPO6edbKQRNe37X/xQ2w+TqfcTFcR46fOc9H8Klr/6qlWvvy6JZCxm1KH7KV56v3BQgoasMBWntrtgdJpYhs3GRPwKBf3Z7KyIvyI+mP43CINQs6elvG29K0UBs8utVykvp01eXCcZrbo5V7Vj1VluvucsNddCb8q1J4HFxBQpVqqbWpUSirzoWcVW20zGHTYkDKvQ3Vtesc4aVfpaZqHOao3zXbCjQy31juUWe8xf7OwoL3vea7JihkigyDr9LMC18pK6OEfODGkTpDwmIfo/jYqUpCW1WepkDbICcdUuUjbgJOHS22TyrwgbCKbmxUYOFh/SSyz5mPi6wIIxvbUGxZovHautb60FF/VgaKEuXhQMw5t0ume90iWN1G1P54JIkwsCtvs2B11AVftt2rk/E37I6kW0JHjjuYgR77TrGXf8R7WFTtX8/AHuvZJbfsLkSVHy3wFnRLXDdjuFZ2/m9tPZdBxFZRu/cXruyuhzePWXbLIjq6dS1ULdBGqPVWJse/H2lDIj5U01wMHmqZfUIhsWy8RXi5UXR9nhwwOeLBe9OleYNKnu3763v/LoEBqfGf+UEP1fnPOvyNTLcA9OD8Nwo3GK7QTtJ0CfPhuJXvkPx9+OPhpUdOpEOm3LoUXCJNZnyZRRmhCcegaLzqR3V3KPRiGS1rHgpIjvYpPHiHey3PnWuE1CRrE08jIGylsjLouRXtZNF0N0N87/Y+++4+wqyv+Bv89t2ze7m2TTE5KQHlJISEgIEHoVaVKkKCBVBBVUEOVLM4AFBREFAenSpUlHILQUAimk97qbTXazvd1yfn/cVRBbFLDw4/N63dfuPXfOzJw558wzT5nnU2mNB/xURlpMTKjdUOP1MkS1ClttUq1CrRqFSlRLSSrRVTdLbLW3EXor16zeYrOlxK230XobJeQ4zne0SXnNCnVaJE2R9jvzzbXQAiQNN8lkeyvSXbNGVdarNMM8s6UtMNYeFmuQ0W6y4Uos0uxFuQZKWiZPi3YxcS2qfcO24AqZ3VuFQZfsAB9ONld8nZI38xWWtRm0dJ2Xkwd4NnKoovomG+7uomTISgccukCQQziLsoYmYULWaBpNUJWmOs3y+5lfwcT9mLg/+3+dljZuvzjrlevZnWkz6NrvL2/2xtV0600szlEXssvB3P197r2MJ3/O0RdxyNmcdBPTpvDElRxz7V/W80eMPJ95PyLTk9Y2grJsapEuJxME8n1Bi3uEZgjkGStlNXqrtjzoo0uwVlmmn5ailEzvOPkRcvJoo7IyTzKZFo//b25M+7fgM6HxL+MfEaJ/sMyGIAhismSTNX/v3CAI4rIC494wDP8mOUIYhrfgFhg/fvz/VP6DVS++qHrJErkIGhtMPvkw8ZX3Z13/OQNpWSO46Cmqz6dzt+zmvIqepMs57CXCFiJFxIolbVLtt4hISMsINCnGVlEx+QbZ7MvqPKvRKstcKqNArjz5oprV2M/pBhr3V/s630bXes6pJtvdji70iDdEXGmyABMdaLN13va8DZY7wlctV+vXXrNFowC54ooN09NcDRLW29F8abPNdaidjNVHfyP0N8IQh3rUz83wnIRcnfXwiqWG20Nv6zRZKVAi0CgmRcc2w7R6QZCWsIOUKkFzo9J7G9TvWWDbpE4qW+Oia7s6qfEez4/Z09bXCmVaI45ecpGhtcs1l0XlVMRsqg69vrTdPjf/RvnhX6apgbdfZtYLzHieN5/JDkx5GUGdMLeY3oO47BlBSde/HMAn7+CKU+jUmb2OZN9jGDeVSx5h2Wzu/h63f4vfXsEexzH8UJ77CUP2zJ5fuzG7QbDXCHY5OnssrwsDj2X1o4z9CptvobSG6t/S5YvKXW6Ne0Q0KLa/Ok/q5hBNNqGPokij0Ylqm3vkaK+P0xcbutDWgq7ee6/K2LE9Pp6H/dOIz4TGv4w/EaJjo6xj+4sfKvMEvoS3cDT+EIZhGATBE7gvCILrZNd0gzCrw99xGxaHYXjdJ9j3/xjS7e0eO+RgEQyPs28+scYnBHmEhaMFG+cxZSgLDs6y6hUGtHdj8yb2u4F42Z/Vt8F3hUJl9pP0KHJ09w31npU0X6nvedG9BtrZ7r7oAT9Wq0agRrs8h7lAT4PMtV6uuKHejwoKhR71rs4K7GmQmKhjjPNL013iZcuknW+kPfRzqNO1Svqt2V60RDfFvudgQ3QT6VAsWzR5zp0iViswzkbNfuwFR9vZ543WrtlbHtKqUld9VKvWLDTSXt7zsk12sZtT9XGk9c7R5DVRKRn5MtrlG6jdWu0yUvmFmo/MlyqIycu0yslLmj+4i0eaz5CMJdxQeombv3CEsfNm6tGNF6/DvLR9h9G7Ew33Xq+84m22VWZDX3sN5uzvklvE479g0R+Eyaigql64ao70/uNEfvGgyC67vn9z3pnOtDMYuzvlfXj+tzz2a0q7sv9xfO4UrnyORW/w7K955V7amolHuPYQH4h6JhKlfHY28gpGnJtl/Yv0J9Upu6dj3YWUfk4kWiRulJR5Qq8JBIbbpkJCbhhKBjFFiZkKyvfRvrSQnTGvG9VLUOL++5d9JjT+ET4TGv88tpMQ/Tbc3eHorpEVLDrKPSjr4E7hq2EYpoMgmIKTsCAIgrkdTX23g0P3U4HXzzlbS3tSQRDoHIS27bWT8qIFwqqEwDxGDaN0MSXFdGohSLMhpPvu9D/iz+pq8rYGbwrkCb2DUBdXCfRU4ydKHGeZKu1ajHeouaarVWNvx8gR08NAJbqr0eRn/iAl4zjjHWKkQGCBTZarcorJYh3UlX10E8ix2joF+rrCOw7Uy0gJz3rPVo0ONMIxxsn50OOXp8BhzpTvdsu96CiHmqWXh71jpln6WiDQbnfHGWUfFVZ7yA0qVDrEeV52p+e84QA76e3HVjhESoOMJCIareloKQehoKxEYd1gb8XajQvnGBhZ6b7McVqqEza/0clNF1ylqYZkK/OeIREpNBG7D2qkea7wlbWC0u7ZSKc3H82Gzv4Rh39DprGL8IeXiA7tItqwXvrIScITvy7yvasE2yr59pH07M+PH8/meWptyWoqL9zPozfzwM8ZNDorPM78OWf9nNcf4qGrqVrH1+5h8MSso/3/xnDbKVw6i1iC8l3ougtL7mTcZSz5Ol3rsynb+/5QqbNtcbbAZkWO0OB1uXZVGmyzKSwTajW0cKkZ8W7CAVEKcrIkTZkCjzyy0LV/xzr2/z0y+Od4vv6nEPz/kLly/Pjx4dtvv/2PC/6HUbNihV8NGiSGHoMHaa+qcsqZvUTWLKIpRt8dGLGCoQfQ8hxd0Ov/6H3Zn9WTUa/JszabJm2bHF2FNkkYKM9pqlwhxzDd3eZe0/Q02FBTPeaXRppsfyf8WX23e9PzVhqiixUq7Gagr9jNNM+q0eQnjhYX9bJNrjNfjqQcW+xtiMVqbVQlItRdiTNMNsTf2MPwp/5nvOwOS7wpRIUe1thBmYgz7GqnD4T/LvOOp9xmiHGmOsLv/Vy1jfb2ZX0kbHW7HAORVu0mUeXSqpQ5STeXe9PD5npefqZNflDr3uAki14eZtU+cReeMM0159xl3bpONl4Y9+KGrfb9ykkWPPSAhoZ2h915tyEnnpjtSCrJ5tVsXE5+kVCJ1N67CPY/RPSm2/j6oYIFb0pXkSntJ9YnELTUc/sM+g76s+sPw1BQv43nfstTv2HxHHY9gBueyTrQt27knBH0H83VL2fDet99ghs+n42yOuz72YqW3skrX+aQ51lxKd4hL8Oo+TJ5va01XCAppZtm7aqM8IwxlhsiEqbkhqFH3j5CqiKPq/HeCppC0ehsqdSHjQafDgRBMCcMw/EfqY7+40OXb+d886WP3t6/G5/lnvovQRiG7t1nHyG65OZoXLbclK8eLbJpEQ1BdiU6bD29p9C+kLJcEv3o+Z0/qydpg00OtdXXRVWJSwltFJEnYmdVLlNob/08aJF3tGnWz1jPuEO5PvbyhT/VtUWzm73jcRs0y7VK0qFGecNK3/E7y1UZqp/bLHW+N/3Au/or8kv7Gq+vP1iqRrWd9JbSzSK5XrFNw4d2PqVkPG2dM0x3jtdd6V3L7SriWOMd71vO9R0HahV3jbf8wDPesFK7lMF2trvDLTXHu6Y73Lf0MMgLbvMHsy0zxWYTRHxRmdOENuvsy/KdZ76XLPCKqDIrgz7u80U1G0usuqFMGAb+sPhoreMe1/sL802aPd+QsmJ/uPVuPXr0055h/lmnSx15gPQ3zhJWVGRNVBMOEQ6aKHXmSZSUiv7sZkFxqeCmF5l6uGg5segGKtZI73qssM+O7z8Dra3S1/9Qasdy6bvv4JivctfbfOOnzHiOZ+7NFuzSi9N/ynvT+X0HvevYw5h4fNZJvuG97LGBx5LbmUW/YtwdWTrcMGDNeSJhoVy7C0VEbZZnqHLrlMqGb6eCuGikVVmvLdmwlAkoyvpk0ukutm37FC+lPyr+6NP4lOae+kzT+C/B7044waL77pOPMTv0srY56UvHFglWr8zyLexTQkmUkZ+n6hfZkIFBj1D2fsx+m/dsdpKMBimtmuXLl6/MMRot1exVpb6i3MWSku52kXxdVNmiSKmjfE0nnaWFbvS2l62RkU2pfYIx7rZQHyXyRK23RkZgqy7iogYpNkG54wwUE1GvxQKbjNVHvoQarW6wwAybJNBVHKFxentLjQrNBinWSY4qzSq1aJcxVmc/NFEgUKvZK5Z5xTJbNCqUY3c7OsBwcz1pgTeMsKsqG2zpyJUSEZHpyMwbFRWTJyqtXb1AKN3eXXOiyaxwgqaWAq++ur/k8euoq3P11XEXXTT6T+PbvHqVm0cMl2ptkwyzW2S+MGqwnhVrCAKR088V+ebFMtf/UOb6H4re/6TIgYe+f5NTKa45i8dvk+42Rub1uYIDDhW96TfCV16Uvvxi1q2hZy8qK0Qff0lk96mk03xlNzas5KHFlHTJpj3/v4OzguMXC+gxgIatXDyU1jhD9+WCu5h5EfN/whfXUPEgqy7Ibtgb9LD6skbVLhaR1qKzdmkL7OTR8EgtQa7SsN6qtn5mPD+V6oAfhKxcijoXX5wxbdqkT+p1+I/hY9E0+o0PXbKd882Z/3uaxmdC478Ac265xTNnnimGYRG2hux/5Tn6zLqJdMCRk0i/zT63s/okCkNKDmbwUwSBpErb/FyTewWyHN9N8qXFxLUKOkhJu7lcqaw55R3Pes3vpMX0NMDnnSlPNvb/ccvcZp599PO2JYbobrm4zeokJMXl20kXg3UyXg/9FanVap16w3SW731GubTQTBs9aqllapDNQ5USdMQ1hcqUONsoE5V39DXrZH/MGr+wyKV2tof3Ha8ZoUU2+YOlZlvbEaW1gxJzbbZCD/0NNMpWq63wtohQRkS1zkKBIg2iHYIkJWp2OF5zusD06ftp/EUFjwZYa8GCcUaO/PPAgq1Llrhrn320VlWRSunZvbsTX5vOddOEv72ToiIaGgQnf0Xs+r9CIxuGVFcKO3eXueVGme9fmDU5tbUxcrToD34i2HmC1N67UFcrNv1dQfcerHiPE8dywPFcfle2ri3rOXsEO45j2ku8ch83f43G2uzvv1yUZeD77UB2voTxlzF9D2IzyOsuNXq69dHdBUIpgTa91Ctwa/gFVcpFkQjbPPLKscL6uPAKLFhKKmnAgPlWrvz0mag+NqHxne2cb776vyc0PktY+B9Ea12dV6+4wqyf/lQEBehfVmjyk8/qes1eWfX1xAuo/zG7XMLmc7MCo/NJDLiFIJCyzUp7S6iVEWgXkVYoFNFikIwpig1SbISkAustV2+rN/1eWsxgOzvQyWIdE/1GDe62wC56CLTISHlNi95yfM1YL1hus0bn2kkX+UKhF61xq7lapEQFhupsrO4KxD1puU0adVfgdGOMVq6XYgtUe8Y6a1TZok5U+k8Cg6xgOUw/z1jvVxaboFxuh7M9IjBSLyP1skWjZy30sqXaFOtpd6sFXlCpTVzCFAcaqotyV1tkN13F5VhglQ0bo4rK67XFcsyaPUXj9BSPVqCXsrJGI0aU/sU96zJ0qC9Pn+72SZO0bdmitrLSO3ffY8JNvxGee4H0lZewuUL0B38juC8I6NIjK8bP/JrIxMnS0y4VOexowfEnCzp2iMfuekRqnwnSpxwr+sRLgh1H8qWLuP0qDjqRXffPcqacfh03nM6ZQ9i0gkHjKQiYO5s3HuL4S+l7CItvYdQFWTPV6yOJbRKrmSHRdbSUdeK2atOkQL2uQZUtyrWEcQWRBiU9q9U0dmcyVhTS0Gz16o/rLfgU4lMecvuZpvEfQJjJmHvnnV666CLNVVWisolYB0T53G23iL75MzYsYuBwDupP3esMjJKuofhkht8JWq2w2jFyVKJIu0BaSpMCbRLmGNfBlPfXMcx4B/rSn/JCpYUu9rL16g3Xw2ILpeX7ol0dqq+oiAqNzve8ITq7wEQ3mWOGTXbS1eGGWGSrd1VaJbva3VGpIw0xSW9RgfYwtFbKamkV0voJPW6GtUGdS+xm3Iec5PNUu8AMJxvk5A8kP0zJuN5cy9XqoUgXuWpt1a5FT0UK5SiQsM42M62WEZXQWX/FVoe1tgXvJweaP39n6+t6yxz4dpbAKBN35pnb/OpXu/3Nsdu2erWbBg4UCUMl2Pm73zXhyisF/yit+T+BzIP3Sp9xosh53xK94oe0tXLCGJJt3P8eeQVZzeWyQ1k2ky9fw36nsnEhXx1F94HcsoLKt3hyD7rtlmULXP1LtnyTwl1tG3WsWj8UkdYmrlWxeUZ6yBe0iysJ6yyrG+Td6ZNpxtebsuHdVlq1ajf9+xd9bNf734CPRdPoPT70te2cby76TNP4DP8ALTU17j34YBtnztSpZ8+swMjPl9vcbOeSQOTte7MCoxBnX82szzM0h2SS1E4M+w2o9XvrXdTBQZGQ0ElMpbXKzTbR7g53mvGa1Kuz1RoLrDFXuya9DTfBoXr586idJy23RLUyncyxWgGusb/+3jfR9FDoVKPd5B1neFpK6JRwlE6ZHTybTqlWpCbsJx22Koy0+1a8hx5B1INhsx+F9dZKdxiG3keuHY2x2BXe0FexdJDWKqVNWj+dDJPvfssdoLfOcr1kjdvM19rhUN+kumMDX0Ra1E5ivmqY/oq0SHlXmwY1mjSrDZtFg5REmFFR38M7M3fVmszlijTN2wg6o9rhh//9LAKl/fsbddJJ5t91l2a8M22auhUr7H3HHWJ528H5vR2IHHOCcMbrMjf8SDB+V5HDjuS7t3Dmnlx7Dpf8mniCSx8nk8n+D312omdf1q+kYRvdJzH1Dv5wIi+fzN73UHE5zTMVpG5QGyPQWUKNdjHDLVasTr1itWGxAcUrvRubKMiJCktz2RyiyE9+8p4bb/z0+TU+MkKk/2Gp/1l8JjT+zXj7V79SOXOmARMnWjdzppx4XGk00KmAPjuEguWvZYnSJhzKhpsZ0BHxshV73kMQsdxhWiyRr00gFBWXVqlR1AqTFemki3LveckGS83Xap3exunnGIfp7i/Ta2/Q4C4LREQ1qlagxZ4G/ZnA+CMOMMBLYYUKTYalRpmWyrUkbBBBqUDnIKIsSHg1FbF3us7O8dBzkVbjxB0l34Agpr+oclGLJM0O280KR9kaLJOSsVtYqCCIiYtYaIv1GkRDTg1flRukJINWGYE97eBIO3rPFvNVWWiLZu2W2eQcVQ7QX412m7WIiWvL5ImFaWVhf8uW72j28hLJEC9hcxU6E0bFYltNnfrhNGl/iT2uvNL8u+7SFolIZjKWPvigrYsXO+Sxx3QaMOAjPilZRK7+mXDuHOmzTxb0HyjYeQ++cim3XsGaJUx7gJ47+AuFcr8zuP17PHYNJ13LoBNorswSNeV3p8fZ1Fwjvvp+sUF9BfJQLapeXI69veRJn5cIMhJBUmnfrbat6sbeUZZHSed74IHlnwmNv4VPsXnqM6Hxb0QmnfbO9dcrwrqZM8VQkE7JaU+a1INw0gGCVc+RjykH0frV7B3a2srgK+g0yjaPabFUrjZRSYGMiM5axW1Wapu0/jp72s9FRDUbZpUeckXNFNdTnQkaXGWOBIrE5YlYqVpKRoEmcRxsJ4cb81evY0km5c7kIC3pCAK5Qcp+8aiTo7l2DOL6iOoURDyQbnFmW6Mn2vlSosD10U6iH0rU10/MQUGelrDYQ5kS31BrQJDvBiWCjrJLwlrnpJcQqVGfyVWX6enGWB97BFlSp4FKfd5gqTBjZrDJAxZZo85LlsmEgViQsTbZV1l6sFsTxVJHzDOJAAAgAElEQVTtgR3nBzJ5AauxAJtrZDe+bLT//jlyc//xq9Gpb199Jk+2/s03pVGP+gUL3DRokC+9/rqekz76hBrk5Ije+5jUPhOkjv+c2EuzBGdezqBRXHEqJ+0svOBGxu8jKC1l2TwWziRaRiTg5buyQgNGX0DzJuZfxy5XkokKNt8qf8fz1Ad3i4hJaJdSaKiVVltgnjGaw3xDBiw0Y1U5kwLuyqex3datoaampIKC+N+/iP/f8Cn3aXwmNP5NCDMZb/3oR1qqqqQRj8Uc/9xzev/qa2ytkppbLUg0kMihewGt52U3LrcPZfKdlE2QUmejy8W1i0silNZLky0yIlaYrESuVZbLsZ8hdnK7Gcbo7Vx7ucsMj5rr/nCl9r9ICRnK1SoRdtUtHG1dUGyTwIAPlEuFoTtSrb6dbJQUtVskZudYxIqg3ZtBi+la/pRWMlGdo64yR+8hKZFUwv3tKbsm2nQOIlZm0laFaaszaRvDjMowY1vHieVBrntircZEG50WFNkSZpzVxruZHY1ObJAJI9Yle7ogFXV/TtqAIGKBpMfDFo9r0ZaJuTyYIm2bpy22JWiwoG24I4M+DkvEfMM2z6+JySRKSIX8HhvaaI3JsietdPTRu2/3fT3+hRdUvvOO9375S4vuv19rJiOTybhv6lSnzJmj88iR/+oj8ycEPXqK3feE1MG7S594hOiTLwv2Pko4cCfO2lfwfycI0zFhblSQ/ACRw9iBVKxkzTvssHP22K4/ymocs7/PuDGYK78qqr5bu7hReFegRaGkftZaFQzQoFj3vEqRvJRMGKcon8ZGFHrkkTVOPnnQX+n1/8f4lAuNzxzhnzDa6uu9ec013rvnHvXrs5nicyIRx7/6ql4tm/jusVJBT8q7ioXzhIUER8smiWvfiSnzJIMqzd5S6Xrt1ivQLC2ih3sVmKLeM96zwkzLlSjwhuEqFSlRZ6SeLrCvhJhmSWf4vXpJwoxcKdGgXRAGtimWDgep0lWdjAahziIeCjrrKebldNIPk82WhGlBkPGdRJ5Lou87QZvDjCVS1kub1ZDxk9fztaUCPxiRccYAjm2rNyvz/ptULFCaihoWj+gbjegeROQK3JpqsSrMiAQZ34nlezDVbmOYcUeiyF6xqFgYeCLd7mvtTZIoimbUCAkpCKOawlDmA1FYBULT4oXmRlvdp1lPUeuf6ao2HWVRyFMZ3lpDpg3zdOrUrqLiBHl5//x6atuSJZ498UQr58wRoDAatfdPf2rEOeeIRP92QML2IvPEo9InHyU45gTRc74pfcHZwndmiY7sSW0FfYaIfPOKbOqRc/amMErjOvb5PN987P2KUq08OJyiNOXrhM09VEwdKxmsRrV2uVrkq9bFGyZaH+6tImiyqqq/hTPG80Ab963DRsOH11u48LCPfG3/LfhYHOHdxodO2M755qf/e47wz3aEf4IIw9BTp53mrWuvVdi9e4fNn8PuukuvSZOEN/+fMLcTnTeJ9lxInGAH9KWmS5H2cbfLBG3WOlSl87XZJE+LUOANU8ywQohc+5ivQrFSW7XYrEgnddrF9dFfQky1Fl/3ojpJtclyQRhTme4qTE7wUriXg+zn+chO5ka6Wx3p6cZMmcZk1B6ttfq31Di1vUGTUF486XOJiHPShao+sKitaIyo2ZwwsS3PPTMLlEYDe3TmmqURybaIp3I6uT9R5KWcTtbklTl+dZmFr5TY8k6xKyOFvhXP97V4njm5pX4cLxANA1cnW1WEGccmYl6INDslU2tsWOWsSI32RJvWbVGVlbny0lFDw4TdIwmnRPNMiUXE4kmDEmnfzs1xTbTWg5qdp9AtteVq26Okib6BBRVZJ7KNaPbVrw7/lwQGlA4d6rjZs4075xwhGtNpr553nrv69FG7fPlHeZRA5LAjRb7/A+GD90pNHSdct0b05nsE0zcIT7xM+s0lMq1x+g3m7B+wZl32xFlPUb/l/YpiuVmNo2IdEgIVOldOkVGHTuLaBJI6q9LbBmXBepEwY4euawRhmoMTHXwgeRYtatHU9CnmNv1X8SneEf6Z0PgEMf/OOy15+GETzz9f7aJF8lHYu7dBX/yi8PYfCtYukWmtEzuCYGNKWEhmaiAVjdjSu5uK/F/Z5jYRG7RLiEmLCq0yQImJlnrLC35tlue0aNRqi00mKVKnuzK99XWPxb5rum96UaVGsUwX7yZHOCh9sLL0BL9PlhufKXBux8a+MAxdk2x2enuT9nRETkBeLOVHiTzliZSyKJenS018LdD3BS5exNJ69niRA15m4qtUtDK0MJt9oi3NtxaSFwQOieWYEI2bVRXxkxWB3hFe3cIRs2ntiDaJBYEz43mm53ZSGE9JJto9EGnyvFZbZewux7VBJzc0lEu/W6J9XpEHmruYmVfqZ5li+28t9GikzCOxUq2RjGka7CjmD0G5SyOdfP3dCFHiW0ivbaK+hXgjQatolK9+dfhHuudBEDjghht032UX0BKPa66o8ODo0Zbefbcw8+HYsX8OkW9eLHL+t0XOOl9s9lKRY04QBIHINy9mpzHS3zxLuK0mu5dj4GiCeHZwX70lKxwrlvDm3SxaRtfds5QieVE5C25VFJ4oo0kglBCIyDPICrlW6h92EglCfQevoCjIprEJOiHhySfXfqRr+tThszQi//v4T5intq1c6dYxY3QZNkxm1SqVNTXKwtCEq64ypjhX5NYLicU4v0zwVhVVeYxs4WBayo/V1G9vmyNXS8hoExdKyFMnI99NTteutynyVHtaSo48OaoVm6+fpHypD5hoQjrMU3FD2ye7PZXxdm6Jo8Ot1rZFDQxi3sgtEcX57Y3uTrc5PprjhkShWhlHhVst7XjCf6PMrW/neXozh3bjsQpirWRiZD7gDx1UwJb2rNBoyfDyZKZ2YVMro19BG1ub6J5HZYTPdefhXUh8YBlTF2Y0yOgmKv4BB3plK+OnEwvIjVKbZPbuHDGdOTUUxDi0J7t0S9sUTxsRxAUCbWnOnt8xIL/Agkq2NBEuQaOjj+7soYf2/Vjuf+2aNW4aNkzY2qrXqFGa58/Pmqz69DHs9NP1OeAAXcaOFY1/fE7kcP7cbJLEI48Tu+VuZr7Id/bL7hrtlZ+llm1teP+EL1xK/RX0RzXpET+zod+vZQ1+Ec2KZGQsNlRdeKCng1Kt6VzPPfl5Hm7k3kpsMGZMvXff/fzHdh3/SXws5qmu40OHb+d8c+tn5qnPgEwq5fETTxREIjKrVirObZMIQ4LAjmGb8NoLBQmc/y3WVLGO9ND+wkm0J/Jk+l2gKTJPWlyLXIFS+TqLCs02yRZdRET8Xo0mXWWENouYa6AW+XIknGucn9nP7Q61o17qJJwWjvBimqmRuMvVqQrSpiXyLQvTLkk2+UJbvbvTbY5qy7NwdqHLFgdKM1GPB11MknCGAvNX5Hmikh+P4NFdOKSEVNAhMNJo4dGdWbYPb+9B/yzNuOPm0NjOie9k/9Y2M7WcbW30D3iykqlvsKnl/XHsFET0DmJ/EhgtKZqSHDWbbUkem8Aj42lIsdtrWYEhTTzg4fVcODvqujcTTnsjcOrMDoERwVq0ZdjaSE5UNpd1m4su+uvRYv8KSnbYwSE33yyDjfPnaykt1RgEajZv9tall3p04kS3l5Z6cr/9zJk2TbK5+SO3GYwaI3LBJcIH75F55kkm7svgDv6Ogj5MPonTfsNV79FzOHOeo8eXs/etuLPowmuUpb8FIlICLehvgFVqg8VyMwmJaFK3bhs5spB4FKXmzv3MRPVn+OM+je35/A/iM6HxCeD1q66yccYMBZGIcX3afGF0o6PHB/rvspP866eJ9isS9h8uWHqbYCbNkxJ0WyQopa7fCGsix2vyjIR2ca2K7CKwUkovrxivVNx1jna4LuLqLDfYu8Zpk6NAnl85yP4GGKBEF3mmmeAHdtE708uaMGNNpM0Tm0MXtJc4J1bglFiuW1KtXkkn9V1b6M7XCqxqCly7gomvsbkhalpjV8M3lvj+Er7Yi6/twHVL+H0lQS4TSvjlGHbI5bL5pDMMLGDGHowqYnMbRU/wciU9YyRiXD6aLw5idRoBb22jzwuct4CWD71QC2oZ+CT9nuPNbdwxljGdGFHM8d1Y34Y48Rxq27IUF7GQ4jzZEOZcggxW4iFUNpMJiTSB4cPzjBvX5WN9DkaddJLBRx4pE4noutNOEt26aWxvV4/B555r6CmnaNmyxaxLLjHj29/+WNqMXPBdRozKmqmam7nwV1mZ2BDhpF8w5ctZlr89T2fVTHqeTGOMaD1tlQqXrxE3XCiQK6VAiRzthlpiJ/UCGSNGz8/uC5lYIqvGFHnggZUfS/8/FfjMPPW/j3+neWrDW2+5a8oUOYmEroVRJ0xstaU2rUshQqKNgaA9ZMouLJut4aA8mT1202nri9oL49bs0k08eH/GDEVl44FCM33dU7o43a5G6uVHbrdBuQaJjjSFcZfa1W5/ha+iIcw4sH2becmMyJJCDZvyBEIjiwL9CkLvdWmyflNC58aE7w+mOKQmzaXLaUy/T9DeNcFzEzjhdRY3EStgSBFvTqE4zoNrOfYNbpvIXuVcNo+7VskyDyb8Vfb3njlsakBSNnVsnJyAHwxllxJ+s4q71xFGshpNQZpbxrBPd65fzNWLZPnRE4wuZnAB8+pY3pztd68EB2W4tSOLfNBGuKmClmYyi9DmoYfGOfro/h/bc/BHtNXXu++QQ6x/802f/81vxFIpT512mjAed/aKFTr17ev1886z4MYbHTljhm4TJnzkNjNvvS590O4il18rev63+dKOVK1k/3M48xryi2is5hs92fMMhtWQuo9kH7ZVa9v/aZtyjkeoTY5VpupphrfDqZ62h1QQN+ONKbasLOeUVWRqDRmy1pIlR3/0AfsP42MxT5WND+2znfPNw/975qlPVGgEQXAgrpddl9wahuE1H/o9B3dhHKpxbBiGazp+uxinySpx54Vh+FzH8dtxKKrCMNyuIPh/l9BItrS4oXdvLTU10jhqDEO78fr8wNbG0JETiSRQ0p3aSvXH5Kk/vruuz2yW073ZhmF9JItT1uhrtQE6qfvTp1pPz9pbjS4alUt3RFH1U+Awwzxugzopd5gq+qGZeXbY5pRMjTUtMbGFxeo25yiOUf+hlU5elL65VLdk9xOKIdIRudlh9kmGsjNxu+zO9YAfDebCDl6kMGTcsyypz2Y+iQWcOyQ7b/1sJbcvy3IG3bZr9txJpfxiMTcsxBt0H0dDV5oi/uouopx0lvEU2Scjm8DX8M7Z/ixqzPpFJpUytTP7lzMooNcBJJtkt2JEMqxaJZGo0d62WU5Oo6amY0Wjn4zi3d7U5IHDD7fqxRcdfNNN2lat8uqPf6xs4EBnLl0q1dTk/mHD5HXr5qhZs0RiH337VOrog4RzZonNXSVYPJ2rDs9SvgoYMoHR+7ByOpXz+PEiZg+ksD07pomJGsaeYWv0ewgtcqruHrNFF3emz9UcyWhuy/Pi04dx42ZersVcDQ1HKCxMfOS+/yfxsQiN0vGhvbZzvvndP25vO+bRb+IrsrrLFpwahuEnFp3wiZmngiCIyrobD8JwHB8EwYdDU07DtjAMd8RPcW3HucNlqV9H4EDc1FEf3NFx7L8Dqxfz9D20Nnvyy1/WWlMjGgSO+vZXjOxJ2MyKKkozhJ+7mj2/QMMW6d6BnDXtyu/cING5WVNxrvbilLX6WmCUpJgh9lTip553ntlG6Wud7jZKadXZVj1tsEbEbLWWqHekHf5CYEwPWx0VVkulIloXZAUGDCviuhG8NCmrJfxwOPt1Zk091cmsWadbHI3smOHqQUzOQ5OsuSOHWJTRCb41m1PeYMYWjn+NuVuz5qUxnVlxBD8az8gSxhdnhUrYzkMrOLE3D67mhiXstgWr2PYSTa3kJIm2ydJmtnS0ibaorFYREIkTjTG6lAX7M30K03ej9iBe2Y3LhjK5jKlnZSlbI+0dik5OE2EonapC6Kyz+n9iAgMSBQWOf/JJgz/3OU+fc46wrEyfMWPUrFzpubPOkiguttv119v67rsW3Hjjx9Jm9HtXsa1G5pc/Y9fP8WAte5xKc5jdNf7wtcx5nY0NfGkQj/ZkHqI9SM9U9O43ldVnX7Nh7rDcQN1tNiwyWyJol5fbpnvX9RxaggjRPq67bt7H0vf/eXyM5qntnEffxfgwDEfhYfzw47iMv9mnT0rTCIJgEi4Lw/CAju8XQxiGV3+gzHMdZd4KgiCGSnTFRR8s+8FyHd93wFP/FZrGmVN551UbwyJ3zGkQl41IPGoy+Ql+O51UmkP2Ha7PHoWCVbNkSvMkRyTF+6VFikKt+XFVg0psyu1htvG6alCjwDa7maG3UKhMpSJbVekjpsUUy+zlfE+q9LJN8sTcZ295H1iePxu2OC2s0bs1bvHsUg11cdEgdPWwwDcHEu2QL+kMV8znyvnsWMQePbhnTdZX/GGUJ3hmKhtS5EbYu0v23KvmZ9+VojhnD2ZJI09spGsOwzsxohMPrsv+PbY358xij25M38yxPXn+55QUsrqCb5xC9Y7Z8vt2457JxCPZ/sQj5Ed5aiNHv55d9cw7mCHFf/32XHQT1/5KtnNNstpR9Sbx+nrJ5BKRSFJ9/eH/llQY6WTS7046ycIHHkA2vFgYGnzkkQ655RYvHnecytde03PqVGO/8x299trrI7WXOuko4SsviM1bLSjrnD0451W+d3w2keGlt/DwhWSiVNfSo41jTqDlEUpDYfFE64ZXyqiVEdEqx2oDPJD+orpIoZb2XC/efxjXbWBVi5zYEq3bPvdRh+k/io9F0+g0PrTbds43z/z99rZnHv1Q+bG4MQzDv52i+SPik0wj0gvrP/B9Az6cBe5PZcIwTAVBUIfOHcdnfOjcXv9M40EQnIEzoG/fv5+x9F/G5g28O11y3+M8ev1D4gEFIXsMprwTq5YxfgL5XehTtkiwgdTO+dr2TSuoT0kmorb2KVLXuUBT0MlyeyiSFrfVbJ9TJV8PzcYrN12FVt2E4i4yTl+HKFVuUNhNX/2t0OYlSUOFqhsizt/UZkGsXU5jqbfX58qEEYnyVs/0z5UJOXc2AwoZVMT1i3hlM5PLWdnMbas4YYdsdFNLmqpWntrAyE7cMZlohJ0/MAxXjGGv7rxTzak7UppDfZLbV/JeHYvqskKoNc1Pd2ZsWfb4TUs5pBeD1vFAAy/+iG/fwp2PMOMXxN7hqIF06rB45H+gzcN684e9aU6/LzCaW5mxiLwcCnJ5bzXX3ir7lDeGCChNs7xJWXmDzZujTjih7N+WOykajzvy3nuNPvlkm+fPt/aVV6x57jlLH33U+ldekVdbSyZj/fPPq5o929Fz5ijeYYd/vb3vXiH11O+yLIKXd+SfGrcnd7/DSTtzx0844Rs8fDG7HsObj7DwQfa8gIppguAVXZpvtCX/CoFAnlb9rDU6stSMYCdhIlDao8q2AQXMb9E2ZgfX/LrCRaf3+Psd+7QjxMcXTLY98+gHcRqe+dha/yv4JIXGX3F5+rBa87fKbM+5fxdhGN6CW8hqGv/MuduNFx4gDD348nL1LWmFmDKA4QOyiWn779axcTYaYVBCZkirsG+bvMaM6j5FGroXaInkqlWqyQHqbdPoGM9bI5BUok4LXlMrFqZsCYqcZYzRBtoSpv1EvTvCJhUyWbNPZptUKioTiQr6ZlfhTetjMmFEvH+zoCJm/5eyZuu86AcilEKKcnmzhl078/geTPxQINGVo/1d7NU9+/kjiuN8fej738MwqynkdhgZf7YL+/VgfCEjfsaRu7PzYK47m7FnMPYcmkIee5NFN9LtL/mQrF3EovUMO5AuxUz9BrOXfqhQSfb6NAZZs1ZD1ju+efNWQZB0++2f2ILsryISjRp08MEGHXywKRddZMZVV3np+9/XXFMj1qOHQXvsYc0DD0g2NXnm85931MyZYrm5/1JbwbARgi+cIHPLz0XO/nqWARC6dOc7N/GtI1h3INE4RTlZlXh1mokxUuWEW+VXLZLeoUxMjVY5CjQZEcy0NOxlc9DDqN3mePWFAyitob7YxZesd84Xuyku+P88MHP7w2m7BEHwQbXklo6564/Y7rkwCIITMR57bnfr/wI+yTu7AX0+8L03Nv2tMh3mqU6o2c5z/+PIPHOv9zLl1szMZowd1z8wanCHoBjcUzAEhx/MjXXCMx6kV3bGrBhWpr680NbIAM84WI2zvSHjDQd71zo5cnzLQS61r/6ZakvaBqtRJj/d2fJUN19JVxsdVpqWaVCajts3WaBre55kMiGSjEmuylW4sNBOC4u1Ly0QxNOCbRFtW+NikWzo6eHdiWWyfoszd2T/btwziTf3/0uB8fewrY5f38+ex7HrkWyszB5fuo7L72RLB/NoKsXMdzsydsiamQ7vyy8eyc7jl3+Z9iT3TM9aS5oa+NmpWf/GuTf/Zbt/mMfJP2PaQ+zwFYaflhUYFx5Lp7hsytnG7N+8Tcms/XjbVn3bG8TjSaTsu2+hWOyj54T6KNj1e99zyowZOu2wg/qKCu888IBk167a2tpUz5/v0V131bJlyz+u6G8getH/0d4uc92HrBlTD2e/Y7n7pwzem8VPM3IPNuSy8BYGXkBLRlB1h8L0sUKBhBwZEYPCFYZZJCqlML9RdEgb/eKsaicI7Xta5Ucclf9xZGR9cdvzYWsYhuM/8PkwR/B2zYVBEOyLS3BYGIZtH/7948QnqWnMxqAgCPrLJvY5Dh8mFX4CX8JbOBp/CMMwDILgCdwXBMF16IlBmPUJ9nW70bhxo9nXXGPDk49Jbtxgcyq7FOgWYVJJKOhCemq+oO8mQdEkRl4lrL2DtedJ5kdVDi6zLVpqbXRHGw0SSPqdNmvsrps6uQJXOUQvJZ5OtXkwdagBiaVCEc+3DfF7rSJCnYIcDWHWA7ZYyu6RuCMaC1zzUkJpPPDWgQx/GWku7BNVn4zaezyrFvGj9fw2w8g40w9j4TIOuoK20Uw4h0G9//E4vPUOP7qF379CeztDBrCpislH8/vbOHoaS9dz7W85YCybN/LWXAb0pl9PNm+lchu1MQb04gcPM3sJK6s46UAee4mXZnPZ8Vx8Fw+/wdEdSsG6LRxzLZ1a2HNEllP99XkI+PGv0U7XMrbUEKto0SKPgozdxxR768W1UqltyLj55l0/mYfkn0TPiROdOmuWX+60kyCTEcbjGpGbn69y3jx39e5t6KmnGnLSSZo3b7bu6aeVT5xo+Fe+8g/rDgbsKDjxVJnbbiKTFvnGxYJeHTf4Wz9n1ossXotqhvbnvemsrmRiGe2dyK9TtjW0qluOPPU26KNXsMl+XrIkHKoy6GVo96UWDhpBeyUrSs1+vMmTL9f53F6dPtFx+6/Fx2ue+ofzaIcf4/+1d97hUVTrH/+c7cmm9wYJJEDoVbp0FFAUpVxAlIuCiihguwr4U69eUa9YAVEBFQsWFBQRpKNI7x0CgQQS0tsm2b57fn/M0hR1vTdKuM7nefbJzszZ2XdnJvOdc97zvu/bQD8pZWGNffMv8EdPuR0AvIYyVexdKeVzQohngJ1SyqVCCBPwIdAapYcxXEp50vfZacCdKM+Ik6WUK3zrPwF6oBQ/KACeklLO/zU7asoRfnbnTr7q0gXhdCI0yoOsW2cgrVlT+nv2EHgNcJsSk/BTrGFG8tPC2aFtRzGxaDFiBQpoyC7SaAPkUsB42Y1MGcc8twWXJoMYXREeqaFcRpDgaEGIw8APOToEgrENvHTX6Wmt0XGqWtBiJTgdsLInTD4MR6rg9mh4MgXGzoIfDinDRM1TQBsGe/fC/w2FmYsgJBDKqsDuhMmD4YlREGJWbC+tgPlLlBt9eAgUFsOcBWAOgEHXwbjh0Kk1bN4FA+6EKjt4o7kQd2HwnUWbsk6vB7cOpA7lsaVMKTrnskNEGDx1D1i88H/vwWdPwr+XwpkiODQbgkzQ7n44vFuZhYUeCEGZAlwJgYFwc3f4ZAVQ5IIiLyQZMOs93D+4hBdftACnaNDARkZG7Up9cXLtWj7s25dWf/87cS1asOm557AVF6NFKeR4vk8kBAjBrVu2+BXXIcvL8T79GN6P3gWtFs3ou9E8+DgiPgG+Wwj/dxv0aAqlx8ARCaYq6NcAmg2FomnIoFROtWqOEDuRgIVIQinkmExjDhNw2sx898QACLTC83ngtUBKOsN7B7JwrkBcboClllIjjvDAdpJ0P+83e/yacvtb99E1QHMgz/eR01LKPyz1sBrc5wdSSnbPm8f6++7D7HbT4J57OLHwPSrtHvrNmUPExvuJj3CiGQReNLiM0VS0qIPLfQrp1KDHRU5EHEUiinAa04Qn+ZqPyaeU1XQmFA8OXIRLE2XeCE4VBZAUfRqNRhJmS2X/5qacKtJR5fKNy/uGuJsFwYdtwayF6zZBVgn0SYBSAbsrIMYDR/tBz8fg5Gl4aBgM7wbpSeBwwYCnYN02RTD2zQWTAabNh/e+g5gwaNcQjmbCqZPgdaIMZrpRakVruMQzbTIo+5Qo24WEe/4OMQnw+keKCMhzg6FSqQ8kNHBNGhzYA9U25V4YGwH5JRATCZpwyC+HlvVg/0loGAuF+VBW6tuPFmVA0wuGKojRQaUFKgyABTQ5VrwBFRAdT4RzHaW5sShjAmf58MN0Ro2qfXUg1kyZwqYXXkCj1+N1uRAoh1oAjTp3xn72LJVZWWj0eoLq1mXYvn3ozeZL9uEoL8daUEB4o0aXrJfZWXhm/Au58H0wGNAuXYemXQd46CbYtQbaRCj5XYrKoSfQ7w3IfxyCrbgbf0VWyL/Rk0keCURQjEZ6eYlHOEsiq4d2x9E+GrYVw+JywAx1BHuXx9Pyvy8p8qdRY6KR5uf95oAa3Fcr+W9Ew1lVxbfjx3Pgo4+I1usJb9KE4vyzVBcV0SYxmLT7PdRx29F290IFlLpD2dIWT/EAACAASURBVNH5HhpFlWNjNQHYKSGcTXRBj56BTKJaVrNczGMnbbASisBDoNSRL4PQup0Y9XaKimI5sK811uJg5Yldh1KUSY9y43ZxXjwAsINOryQO1KIEsq3pAcuWwWtzle3N0uBfD8BNPaC8CrpOhIwccBvgnYnQpTGs2QefrIVtexVn/iVuOAfKpGgtEAdaA4TpoeQsF1xzEb73hb72PjR6MAXBgBvAZodvf4TAALCWQJN68O8HYfUWeGuR4o+Ni4TcQhRhCuDClFkP6AIhNBxKqn3H4hQQAjqNEjjotACnJXi3QFwywhBCo8AjHD0ajlZ7ApPJSmXlrecrA9YmPC4Xax57DI1eT1zLlsS2aMGJFStY949/EJ6ayt0HD5K9dCmrhw8HKWkyfjzd33zz/OfLjhzh2xtuwHr2LCOPHyeoTp2ffYc8lYl7UF/wetFt3ItwViuzqUwSIsqg2AsNwqBRqdL1SyqHkD44m75LBv0JoggrSQSQx2p6sYybKaqKpHJTGIcsrWBUBjg9YE5n8iQvrz539VT2qxHRCGgnqefn/eaIKhq1kv9UNLxuN/M7dyJv5y4S0tIoO34cF8p9dGgDSIjTYhjuRdtaQgG4csy8PWYEHczbMGLHjJVKggjChpVW7KYNZbKIKgIpktFkiRS0eDBJLescHSg/EYnrlBmt3kPLQC27KwAPxKCMskg9ilicuxlrUIRDg+J800CgB6wVwCr4vx7w7FwQTnhkNHy9HjKyoW0TcARAxllY+i947Vv4brdvn3bQloLXBXUTFSd1aZXvR5f6/gajCIfR93JD00RluOlwLpiKwJ7lsysE4uuC1gU5RUqE+PDrIC4Z3l0O9wyBp8aD0TetNicfps+DuV8qznNAEcwgFME8hwU0VUovy2CAssoLTna8oHG58FoPQHgb/jHWzZwXsomNdXDiRCbjxoXzzjtdf/f1cKWQUvJmejrlGRk0Hz2age+9x+7nn2f7tGkAdH7rLSJbtYKqKlYOHozWaMRRVkb6XXfRfc6cy+7Tu3Mbnn5dETfegva9zxCZB+HeHoroe0qVJ4np8yDjDQjeqRz/kFcpbdKOEkbgwoARO1aC+TcPUyFDkTZYtnIozD8L31qBQIwxZuwFYX/WofqvqRHRMLaTJPp5vzl19YnGX3xe3K/z/TNPg9yLVkoKjh/HDdStZ+bvXSF1DAQ+60HXVuIpEnjXwO5BDelu/oFSEnCjx4MWQSwZXEs406njvZ+DnqbYRADZIhmt8ODxBrLS3gVHbhiuU2b0Ala303J3PIrTREChxicYZ6DuTtjcGSYKYDfoqkC4wCCU9gEVKF6iA/DsLMABH70A/34IDi2BVx+DA4VwMAsiNLBlB7w0Cp6/HW5uCKIAjBqQHigohO6t4Z9jIMoLCdGwdC68/jA0TARRBYEVEBQEJ0oVwdDkgf0QmAQE6IBcyNsKOQehT3ulY/DlKpjxElRngtmtDFWdIykObmgPmiKI0EOLZIhwAkdRehRZoM8CSpQhs6pqKCkCnRvijEAhmDwSr8YK4W3QaSWl2WVUV3s5ceI0JpONGTOu+XMuoBpCCMHA+fPxAgcWLGDnrFm0mTKF2J49qQKW3XsvCzp25Iu+fTEnJTF4+3Yajx3L0fnzsWRlXXafmnYd0DzxL+RXi5AL5kJac5i5CgpdoDeB2wlffgI3b4XgQcrDQuaDRBzcRzZtCaIKLwGYsZAoT4MQZGpSCQ8oVqbmYQGsOAoruG1UKbm5V2l2vv8ENWHh1c9/0tPYM3cuK++/m4QoSE4W6AskbeuDYSiIpiC14KrSUtIwGFEiqEoKxBYdwEHZnjBxlnSO8QGjOE0r7L65dQ5poNIbhFHjRCO89JLxfCQbkJMdSN6BUAxCsLY9vLAQvo0GNErgmssOJ/dDPy18+giEmpWn6pufgxUnIKAvVHmhuRsOzgGNFTw+r+k1zWH7x4oDPPMs9HsMzpbA2D5w6Ais36FsM2iVEQUAjQXIVcp9NGsLh4ogPBRefBRWrYEvloLDAe1aweRx8MGnsCoLtIHw9xZQtxE8+6EyzGSygCMPpEv5Pbp4xQmutYLXrXx3s8Yw6jaY9TG4nFBYoESHhwaBwwkGk+IvsVjB6gAkxIXD84/BsIEw6y147l8SS5kVQ6CHUSOyeHf+dnr1TWXqE10Z0DcLl6scKU+xfHk7+vevX0NX1p/LJzfdxMkVKxBSktSpE2d+/BFQ5hk4Ue5VPV54ga6PPUZVTg4fp6bS6I476DF37mX3J71ePIP7IbdsRLduB6JJM9i3GSb1hQiHUgwlMRbueBbc48HkgdOwvucDREavIphyQMMp6jKLiQRKK1syunDyWGOYmAHZ+SCSQHoQAnr1CmDgQDMDBphp0KB25qiqkZ6GoZ0kys/7Td7V19NQReMnuKxWVk2aROGiefRpC3XO+Q0agBwAIgTkfuAgVDU2sXZ8d8q9oXTRbEaHh0qCMFNNKenMZjQOPGhkNIddEYRrywnSVKEVXm4nnfXeuryfI6naG4FeCD5qAY99ClkJgBaGJsBAAXf8C5qmwK7ZF4ZxpISKamg9Ec6cBY8ZuLiiqMY3auWGHi1h9wEQMaALgmXToWMTKCuH+6fAwtUofgMgsASs+WAM9OV5ikAZEisAPEohOL3JJzBulLsVoA+GhGQwm8FkUtzNx3IgMQCWf6aIzSuvQt5Z8MQoD64NQhWfCmbAoAhFeaniK/FcFOIphDIa5nYqQX7PPQo39AWHQ/L44w4+/dSCRlOF13suosoJnGHbtmuYMcPOokXVwBHuvFPP/PkD/L1sah1Fhw8zp1kzgiIj0er1eIXAVlxMlFaL3eWi0q2U3mo9YQL93niDTZMnc/DNNxlx7BihqamX3acsyMfdtSVERaNbux0RGAi7NsCkfhDkuDDZoF4iDCgAr5sMY33Wt+9KB9dWQvSVeNDyGpM4QzIhtgo+Xj0WPrfDx6tB7wFXLzp0cFFeXsmxY8pc1NRUPYMGmXn22UgCAmrPgEeNiIa+nSTMT9EoVkWjVuKvaFQVFLDy+o600GfRIAbcgYLDOZKwQVC3B8qY/kYDHl1DDg508WG7UWQIZYZKINX0kutoIfYhPAbmacYQJmJJlQ140RmASwraG/eTKMz8i+6sk15G51up3BGJkII4CXm+7K0aCd61cFscfLEPHL7CRDd0hHv6wN8fVJ68Rw6CH/YpgXQEoNypoxRnsEkL1rMoM50koAPhgbdfgLFD4KPP4cH/gxKpfLZdQyAHdu6EYSMgOR1mzAc8IC0owuFWljVS6eloNNCzF3TpDpZqKCoBux3sDqUnEh0Frz4HsTEXjrHdDi/NhCfngMYOGMCr4XywkxBgDoQpD8FN/SE8TIm5+PBD+OczcOZcQgUpUbzyVWi1goEDzYwZE8wHH5ziyy/tQBjBwVBZKYFSkpL2cPToXZjNtfMJ11+Wjh3L/g8+ILpZM4oOHWL40qWEx8dzYOZM9i1cSJXVqpQVjoqi9YMPsuuZZ2gwYgS93nvv/D68Hg/O8nJMkUo+Ku+6VXgG90PcPATtu58iNBoozIVvF8CS58BtVYS9KdAfvBkG1qZ24mTbZDqwjRCDhXJCeIEp2AngwJ7W5B1MxDH+ENiLlckM1dfy/YYgkpJcrFhRzfLl1SxfbuWll6J45JHLhPtfIWpENHTtJCF+ikaZKhq1En9FQ5bmI29PwOWWZDsgLwQ63A+GIPBmayhtEoS2+Ze8r1nORtkeIw66e7/nhCaVM6IuDmnEiAO9cHG2IIm8vM546riprLTQJXk3UcLAC/SkSuppX1xGwZYo5YZuE8SWQYFQnvD33gzz5sHLnwNmaJYIp3PBIlESyFdxwfl9DgEanW+VFgxSidnQGMEbAnggoFoZMhrYHRZ/6dvmhKgIpYKeR8MlXi7hgWF94c6RcPQ4mIxwy40QGgL790N4OPzCA+xvMm0GTH8TtBI0JTByGGzYAd06w4tPQXwcrFlj5fhxPa+9ricjA9q3hyGDFcf3xo1lfPllMYMHBzNzZgRxcXpGjdrDwoW5vm8woYTyGIE1bNhwE927p/xnxtYiLDk5zGzQAI/LxbAvviB90KDz2zxOJwu6dCF/927MXi8GQOh0SLebbm+/jb2oiLyNG8nfvBlXZSVtn3ySa55+GiEEnjdewvvkP9A8NAXtk9MvfGFFMUy+Blmco+TvejgZIk8ivoLciDgq7zJgjHJhx8hh0vlI3oHLq2fH1i4EfJRF9lvFENUCUqyIFqlkTTdQN1bZdZ8+ORw65OTUqRRMptrR26gR0dC2k5j9FI1KVTRqJf6KRuWJg5y+pwVHsiWdHobENlAVYCQ/LQqHycApTT0+YSRVBNGRrbSSezkqGiHRkueNJdvdHJvejUDg8pqwoseWEUh4bBlCp6FvRU9GRgcy1FbGph/DFcGwCiUltRk0dWDt9dAjDu55Dd75GnQecJcrjueMTMAN5mglsK7QCgG+oDi3ksEBrVtJGFhSCJEBSlR0eJKXMrtLuetbUZSlGKX3EarET4SboU6sIg5uJ/ztZpjxFCT8Qu45p1O5bgyG/2zaqssFi1fCNc3g7nGwdi3cNx46dICjR+G7lRXs2V0IaElLS2LGDAM33aT0RLZvt9O16xkMhmqqqzMBgcmkx24HsNKwYSBvvdWJ/ftzmTz5G+6/vw0zZ169w1I/JePbb9EZjdTv8/N65tWFhcxr3Rq33Y5Go8FbXHx+gh1AWOPGxHXujNNi4eSiRaSNGEGXN95AazKhnfYQcsFctLPeRTNqDKD4PbyznkOz7CnQSKQFxOPg1WvQLvZyanI8mhQPDn0g1QYT8+UYTnlScbkNpNmPM79hOs4OaTCmIeig0V47R59UxnzXrbPSu3cuc+ZEc++9tWOGVY2Ihmgn0fkpGm5VNGol/opGwYE1nHn3eloP8YJeUJYSQmV0AG6hYzkDWEcvoihmlPwQqwhjk7wGiwxhh+saDnuaAoIgLMR4izBKF2aLB6/GjvRI9nzVkSpdGCRIZaxegqZK4C1BieNMB7ZDvwjw5sGqXWAMB0chaAp8wXURkJQAuXpfDAWg9cL1HaFeJMx+AZJCICcbhA6ky4XQVqARFXjcXrr2jufHDUEQi/IADqTVgZhgOHbES0mp4JaBghefggaX6UGUlXlYvryar76qZsWKagwGwfjxoTzwQBhxcb+ckcbrlaxaZWXbNjvjxoWSkHBpW7cbpkyBGS8ryxpNFV5PHsJXwVCnEzRuXIbHY6d9+whWrjRTUOBAiL243Va0WiMeTygajROvt5xNm27kq68O8NJLm2nVKo6NG8dc9cWBfg8F+/axYdo0Tq5cidftVpRWSjxcmukuEGUmrROoAJoPH06/6kJ0m39A++VKRGQUngfvQe7YSmbDNNJic9G5bXivaY7mhgPI/WDPCaBwanO8hjNYCGI/LVjiuhWbCEB4JIeXpJJtbgnH7eAwQUMvD4cdJbqPkyJpZ23nWMrz4fjxFHS63/kAcnq5cqHXua6mDl3NiYbwUzSkKhq1En9FIz8nE93pDhQGRvFdXC86xW7mpGzA5wxBajTUlyfoyyryiWM11+OQRnbZOvPMsVBedwRxSHqQboG7/KIblEdCpVPJdGvUna+GJyxC+Qf2DTH9swVYt8OLr3JJUBxulNmLYSh+BbsXtGVoDBq8znBwK8WQEEqPBDcERbqIDC3mzKkqX+yCGZPJid0uEfpkUptrOHFW2Z+QUDfeTs7RHOom65k8KZRRo4KJiFCmXxUUuFm0qJIZMwo5fVogpSAuTsvNN5spKvKwZIkiHrffHsydd4ZQt66emBgter3AYvHw/vuVzJpVzvHjigPUbIbZs2O47bYgvvkmg3790ggIUAIwDh2CvXttjB2bi9drw+nMRvHEJqPRaOnQoZItWzRAEHr9cVyuPG6+uT4rV1oJDNRw/fWBJCYa2bjxENu25TJ+fDtefvm68/v/q2EtKeHoF1+w//33yd26FVNICBqHA6/DcV48dCizr7wowhHRpAk36xxEF+crDqiwcI70H8I3s98iKjaMsdc40HpsyAe6ISJ+gG2QPaAe3jQ7VNWh3GxhLneRf7gu7qaCgwdakv2KRLu9FFfj1vA3IxwqoFXCZlLGBpH/bQBbb4rjgw9iuf32XyiKcjlsxfBJPUU0RmaBsWbyXNWYaODvbE1VNGol/opGeeYGbslzs7OqPSN7vEuSKZedtEMrPdQnkx+dXTDrq4kWJQD8aOuG/dE6lHh10F+gCXSDTct1VsF3XwA6CYs3KEmSXE4IToGoZpAkoCsQCQNj4MWBsHkt3DtV8Tno4sFdiuK78HI+qM1UacVeXMi5bGi9esdytiSEo5kojmQ3TJ3mYdFnZ8jP93DPPSHceGMYfQfocVltQA63Dgnn4w+j+Hq54jeoX9dDjx5nsNm8JCbq2LHDgdEoGDTITH6+h40bbT7hcaDXV+NyWRDCTqtWITz8cCrt28fwyivlvP++Bbv9wrUUHAxVVV6k1JCaKnC7i8jOLkUpD2AmNdVOZuYKxo5NZ+5cJU3Ovn0OunXLwel0Yrefok+fMN58swXDh+9h9+5whDAgpUCIAqQsJSVFQ3BwEDk5djZs6MjGjRlMm7YOKWHevIEMHdr0v710/mfYMXMmqyZOpG737nSbOpWKo0cJTkkhslkzKjIz+e6WW3BXV+PSarFptfRMjKT1gP4U3TqSD2+4kdhWrSjNyCA8KoTRydlo9BLvrcFomlXiOKSjYExbEC4qvRaseYG0yTnAP9Onsj+oGfkPV7DrrTjM/2xLRXYi9BPw/Fq0Rw7TO7M+W3umEOMO4NjBemg0fvY2tjwK+18GJLR9Gto9VSPHSRWN30YVjYtwevN4yLKQ78t6Ui/lBBrhJY584sjjNMlYZSBWWxBej+CYvgFnPqwPi804xqHMKjLCUIfgszkoYdzbKiFrA9RrBpU2KM6ElCagST1fNcRgVlwNlhIQBqWanrsYdGHgzlT2+cgUN8f2FvP1kkpSUvQ0bRrN+vVlWK12IAmEMkb86quSr5bksmWLnXXrEunSRZlHO/VZeP5laF4vn6OHKjlwIJlGjQxIKRk5Mp9Fi6rYsCGJrl0D2LfPwdy5FSxcWEl4uKCwsJiqqjKgEJPJS3R0LDk5DmXyEoJt27rSvn0ERUVuNm2yU1DgIS/Pzeuvn8bh8GAwVFJRUQVAVJSe6GgjJ07ocbmiUA6Cg8jIQAwGHWVlXsCN3X6K5GQtJ070QafT4PFInnrqONOn2zGZ3DRseJrDh924XEY0Ghg0SMfatfuoqHDQtWtdFiwYRP36tWdGTm3h4Mcfs3T0aGJbtWL4ihWYo6PPb3OUl/N5y5ZUnTkDUuIEYjt1IufAAQBaDR5MaPPmrHn8cdIaJzMkOhMRCHIYiDQoD21FVbqW8MMHMVc7cB/VkJ1Qh4f6vIgjH1YmBNLhoTK2Wf8GLXQQ4IHxS4m5zkadIXHsuiOFpxbNIqZzG/6eMIIAaWDdD9CqOURG/OSHVOdh/6gxz5z5gGhTMQ8mPujrbVx6ziVO3OSip57fx0gVjd9GFY2L2FpewfTQlUgpyD1Tly6hG9ixvxNnbUmE1atEq3VwOLMFTrsJjZB4c1Em6OgALwQthaozAiJBeww8+Seg8Ag06qMEN5zeBZY8aNkWLAkXvJO+FCDkezEGWtEIG7YiJ+DGHOimutqLTgf/+Ec4TzwRgdEo2Lu3iv79C7BaJY2bVFNZaaOk2ExRUQBxcSWEh9to3jyEli1DyMmxsXJ1MbPeaMnIkeW0bWtk9epE5s+3MG5cIU8/Hc7o0YFUVbmxWNwcOWJh3bpiPv00zxf7UOEz0EGzZrGEhFjZvDkLaIwQxTzxRCqTJ7cnIkIRqcWL8xg8eCeffdaWHj0iqFfvW1wuNy6XBq1WhxBa3G4DShCIBpCEhurQ66G4uACDwcGhQ71JSwu65PxkZ1czf/4xnn12N88915pvvjnF1q056HSVDB3ahIkTO9ChQ2KtzClVWzj+7bcsHjKE0ORkbt+48RLhKN67l0Vt2hDbsSMFu3djcThwA1FhYWg9HtzV1RhSU8k7fpxWkXBDI5BhIO4EGQdUBSLCrMj1YLMZCDzlZNZLd7Mmpic7bjZRvskDWg3WyGjGv7GfZ41TGfzgI5SPqk/hm8mEh1bw9o7/Y579XqybEyn6IY/ywx2Z+XBTul6Uxf7Q4ucY9uEgDlcoPclPOg1n+KAGcM2z59tIJKU8gpWvSWAzWi6a9/0rqKLx26iicRHHKt38I+ALelrX84/vZ+I+rkMu0yi5LoZolRTcOtCH2nHlmCDAF3Xm9MBJLSx1Q5kF8t0QGA4F20DjRR8Wgk4mYSsNB/cWoAKDqT5SXweXLQc0DvCEgOdcXgEBGOjWXUejhrB48W4SEtz87W9N2Lq1nE2bSikrc6F4s5PRapWhI7s9irQ0K23burHbPezdayE723bJbwwIiMBmi6FOnWpycgLQau243ad/4Yi4SEry8uSTrRk8uA433LCCnTsLcbvzGD26BTt2BHD4sA04iF5vIjk5jhtvTOXrr6twOiWvvprG+PFbKCmxkZISzAMPNOHo0XI++ugYNhuAlqCgAKqqqtFozISEhFJebuett1oyYkQSK1acweXyEhcXSHx8IOXlDnr2XE5srJacnCxCQow88EB77rvvGhISgv+ja+OvyOmNG1nYty8pvXrxt2XLlLgMHz9OnMj+mTMJ7dWLrHXraDhkCE369GH39OlUnVauE09EBBWlpSSZ4JYuDQgyHUdzLxAKcn0iecPaYUnfS8rkPKpamZlw+6tEl+bxadtk6ncq5u6R67B3imVtVHfql5/i26dT0R0SHFrXmsdnzqHNfcdZMKMd3z6WSHS6A4cYyj0jYjhhAovVxg8/anHr9cjOAg5KjKUutl3fg5YTvgGTEntSyfvsyXyd996awBtP30Ww2b8HiZoRjTYSNvnZOlAVjdqIv6KxI8vLvUt28N6DdzBswRKOhTWBmSijKAOBevK87wCPgM2F8MkecBjBUA+qnL7pKcIXR3EWiAMSQASBcDLwJg/Ce5Bly/J9voJglHF+G527htGxUyQlxQEMvsVBdnYeTz21h9JSn/ccSE8PIj3djMmkQaeDAwc87Nt3rv9egFZ7jB494unUKZ55885SUuJi3Lh6xMWZeO21TMrL3QQEpFFdrcdo9NKuXSl79hRjs3lISzOj1TrJyCghJETPO+90YOjQ+rhcHmbP3sHjj3+PwxHJ0KFJfP55P/buraB16x/QaOx4veUoXSYTSg/CglJAQ0PfvsmsWNETrVbDnj15tGnzDtOmdScmJorp0/dRUGDztTXRpk0SqakGvvnmNHb75WpmeggIKGXy5PY88kjn870bld/Hrjlz+O6+++j173/T6dFHz693VFTwdmIildXVBERFUV1aSoTXS3zHjjS7/35OL19OxsKFkJREWU4OZqORW01OjL0l5iAI0oAnQMPRl+oR8W0Z8R+W8tnsW/i47ggmuGdiLzfzeegQqrVmxBlBXEweuQGJeJ1QuCKIjGcSqB98mD3fV2NMCsNdWEpMMy95ptF0S/oM7elq9sYPxHFTBPf1fY33Vo+j7Msoksnm3akLqKjfl+tiNGRaRtOzzULKikLYtcNCy0b+5RurGdFoLeF7P1uHqqJxyc6F6Ae8jvI8Pk9K+cJPthuBD4C2KGFrf5NSZvm2TUEpku4BJkopV/qzz8vhr2jYXfDOnntp0Wgv13fbgPNRk2JVEdAe+ATYhhIS7a6C05uABJABgBdCI9AkheCNF7DxNDh8mcs0ySQklnH2zA6uvTacV19twahROzl61IYQaUjpBrIBMJk0hIbqKSg4N4XKQY8eEcTFefn0060kJsaTm6uM3Wq1IIQFKeMJDo7FYjmB12tHmUTpJSwsiFWrruOaaxRRKSy006/fJvburUKIegQEZFNdXUHDhmFMndqY2bMPsmNHEcOH12fWrM5ERBhZseIEDz+8iqNHi+nfPw2tNpINGwrIzBxGTEwAvXtvYv36YqQsIT4+gPx8JcR82rQEFi0qpqREcvz4dYSFKTPKRo1azNKlxzhz5kFCQ01UV7t4/fVDPPXUzvNZbfV6CA724PFUU1GhCA9oiY4O4oYbknnxxR7ExFxaQ0Ll9yGlZPGwYWR89RV3bNxIYkdl/GfbK6+w5uGHz9fOKgfa3HEHN77/PkIIpJRsf+IJdk2fjkMIrFIiUKbvGoCUxibaBdgRt4VRdFskje47SWXHQB6Z+CKl+gjcGh2Nyo8xYeo7VDc1sab+9dyYtIyHsh/D3j4SQyRkvRlF1gvBDPdOxKSr4kj8AEKfj8fVOwFRYEPfdxuW3FDmzP+QALODySsfJXNVHZr0OkBcnZPYfnTxzZ6xnD6TSuxdFpY91JB26f71RGtGNFpJWOdn60hVNM7vWAgtkAH0RalzuwMYIaU8fFGb+4AWUsp7hRDDgVuklH8TQjRBuUW3Ryn3ugZo6PvYr+7zcvgrGpmnS8nUDeKFx/6PDet6IXsLSLNBiA5OG2EFGDyg09mwntkC1nDwBimdhcpjSjhzWQS4bXB0GcgYNJo4AgI0LFkSTWGhhXHj9mCzeQgO1jFkSCvee8/BSy+Zyc4uZtask1x7bRQNGgRRVlbOkiVbGTasKd98U4LLJXG7rUAxN93Uhjlz+vDcc7t5880jrF7dnz59En1DUhVs3VrC0qUnWb/+DH37JvL5570ICzNSVeXi7rt/5JNPMgEtWq0GIdy43co1EBioYdSouvTvn0RAgI5XXtnKqlWZNGgQwYwZ1zFwYEMyMipo2vRLJkxowrPPtqVly2/IyjIweHA0J05Us2+fFSW9Rx6QwLx5vbnrLsUReeZMBfXqvc7EiR145ZXrLzn2Bw8W0b//EtxuJykpJuLigomNNZOeHkWbNvG0ahVHSIjxoytscQAAEdFJREFUN8+hiv/Yy8uZ17o1SMlde/Zw+NNP+e6++0gfOpTUTp2IbNaMTbNnk/3990w6dQpT2IUAvMxFiyjcsYOjCxdSmp+H3eMlxGTAa3eiBRJCdTQNdxMRDZGRsOyONqwYdDvdjn/LwGfWUm3T8kmRie7X1kE3SEuMpoA2j75GxAgjKRO8OAp05E7VENXFQsidJlzlWk6+Ek3yPSVog7zcsHgp4xt/RGFpJCHBlXz+YgSnnREQYODTow9z7EQ7Qge6uOXYTCYueZTWTfxLUlkzotFSwio/W8eponF+x0J0Ap6WUl7vW54CIKV8/qI2K31ttgghdCh3m2jg8Yvbnmvn+9iv7vNy+CsauXlQp4tEIpQbf0U+WHxFwEMlNKtPXbuW03u3g9uXyS01Cm4JQv/OOlJivRw/sYthI7vz+cdlKA5kUKrZSkJDD5GaasJikXTrlsDCheG0aaPl7bfDadw4jGHDtrN8eT5z5zZkzJgvSUurw7FjbqTUoNcLhJC0bGlkx44TTJrUnddfP8FDDzXj5ZcvrXX94Yf7eOed3eTmesjKkhiN0LixAYvFxMmTlUyZ0pLk5EhuvTWRkBAd06b9wIwZO1ACRC7kJgkLM/HUU925775rMBjOFxtl3LiNLFhwnE6dYti8uYAWLVLJzXXSuXM4a9YUM39+Y6ZO/Y4TJwpo1iyGadOuZejQJjz22Bpee20rmZkTSU6uHRHAf3Vyt2/ngy5diGrShML9+0m78UaGfPklWoPSM8zfu5e3W7em+1NP0ePpp3/++Q0b+KpnTxqGmzlcXk2V73YiUOaHRJhhbHdYfwwy3JDogRtbwJJsDZmlggS9oPUtqdQfforlFX2Z9fY1hFafRj7XGk+7WLxOsMxyUP58KS6DB8uErrSflInB5GT00o9Y9HZf+jyyld0d2nE8OA2AQw8mkL8pnLf0U7gpZC1bpx7g+mv9m0FVM6LRQsJyP1vXUUXj/I6FGIJS6Hysb/l2oIOU8v6L2hz0tcnxLWcCHVAEYquU8iPf+vnACt/HfnWfl8Pv3FNSktRkM0XFTlzFAkgEXSVa3Hjc4YADjbEMr8MIhEO9CMRAMzTV0GvpDtZ+W0p6ehUWi4GzZw1oNGW0bRvIgQPl2O1dCAlxkZ6ezbFjZVRUJKP4O3YCNhITA7nxxrq8++5RPJ58AgIiqK42oNUaeOONztxySzzDh6/nhx/yiY/XkJfnIjzcwPHjw4mMVMb17XY3kyat4J13dtO0aTRJSSGUl0t273bgcoHRCCtWDKBnz4Tzvzknx0LjxrPp3LkOX389nOJiK4WF1ZSW2mjTJv6yPoOcnGoaNPgcu93DW291IT09mh49tgDwyCOpvPRSE9xuL59+epDp0zdy5EgxDRpEkJdXxcCBDVm4cPBvnguVP49zQ1L1+vRh2DffoDOZLtn++eDBnFyzhoknT+JxOqnMzcWSm0vhgQPkbt/OyZUrcTuVdMcajQat14s5Lg6EwBwby8C4LMzOcr7fBt3aK+ntl58WhE+IZ8cTLu7sUA63JZHa9hTTqx5n+WfR3PDjAppeH0BiVja7wjvw2U0TWKfpSzfPehYlDuWfjaaSY0oiIfcsufUTCSq3MLr0E5bZryO7ST06zl7E1OWL2HQakpZvI7nOb9dSh5oSjeYSFvvZuuFVJxq/nPvhv+dy0xV+qlC/1OaX1l8uq9llVU8IcTdwN0DdunV/2cqL2Li5nLNHzwJ1gEigDNwFKO7YSiAeryNOaWyu5qE7TYwcqGX03btYu/0QkMzRo3qMRgF42bRpAB07RnHqVBl9+qzl5MkWWCx1Wb++D+3a5XDHHYGMHdubkycreeutQ7z99hHfT4+hulpDnTph/PBDP1JSlKmna9YMYOrUHcyYcQCdTkdZ2Vk6dJjLxx/fSmxsEEOGfM6uXXk8/ngXnn22FzqdcrgyMiqYNOl7vvtuHwcPnrlENB54YAUej5c5c27AZNKRlBRCUtKvR+YmJZl5++2ulJY6uOeexkgp6do1gu3by3nwQWUYQKfTMGpUC0aObM5XXx3luec2cupUOY8+2tmvc6Hy59H+wQeJadGCpM6dfyYYAN2ffpojixfzUlTUz7ZFNW5M/d69ObtiBSaTCZ3XS99PPqH+rbdeaPT9p/DvEQwYmYTIzSHLHYLbWUnelLPUG5LCqtV2xi8/RW5iFFPjXqDByCFMvG4FY3a9za6eHViVMoBIWzEvH3yQSZ1fBxcMd33MO+57OaOpw6SXZ5OxMJAD/VL4Z9/nWBB9O+snDOW9/npaff8dCeGJf+ThuwznqjD9b6IOT13E2XwPifH7gGAwOTHGmoiKM5NbEUaMfQmFp/Mg9AawV6B1HcTj9hIUpMfpdPsS+JlRsvlIGjY0c+xY7/P7ttlcdO++lR074tDp3Gi1gjFjTqPVusnOrmDZsgzq1AmjX78WrFpVRNeu8XzwQRc0mp/r5MqVOeh0AoPBze23LyEnx3I+t9KCBYO4+eb0n31GSsnAgZ+wZs1Jdu26m6ZNY/jqq6PccstnvPBCbx577L8rf5qVZSUry0qPHj+/sZz7/tJSG5GRgf/V96hcGXbPm0d5VhbBCQkEJyYSkphIRIMGmEKV9B3LBw4kf9Mm+i9dSnzXn1xLLgeMrgMVRdCwPbyylTNbtrDmjluxnyzAOqQTAzK3kxrlQdMFtNfB0co0eju+xyKCubv8dSblvUXdermQACs9PdnbuQV6jYuc/XUwjnXxQvSTvJJ9Mz+mj+C9kLHMnfB3fmjbjWszNtDc+hL9Wvl33dVMT6OpVFyy/tDyqutp/JGioUNxWvcGclGc1iOllIcuajMBaH6RI/xWKeUwIURTYCEXHOFrgQYoj+G/us/L4XecxrFqmjQ5SZ++BiY81ojFa+Gb72FEf5g5RbJjx1lefTeXNWuzKD5xhICAIJKSYjhxoozrrotl2rTe9Oy5BY8H3n23JWPGXNrD8Xq99Ox5mB9+MKLV7iEwMBODQUtgoJ4xY1rx+ONdf3eepIoKO5MnryQjo4QFCwaRlvbT8NkLFBRU0aLFW8TFBbFmze20bv02kZGB7Nw5Dr1e+4ufU1H5Ldx2O16XC0PwL8xSeu9x+OJF+OdyaNdf+YzNxtc9e1Kyfy8VnXowSbeSo644SnX5dHkCynRRuDWCeFGEAGweE++evoM6YbvYvsGId0JnjMJD4plCYh7Op3/oBvKdkRiEk2s2vUzMglAi+wpmervRMPLyDzM/pWZEo4mEj/xs3VYVjUt2LsQA4DWU6bHvSimfE0I8A+yUUi4VQphQKlq3RilxNFxKedL32WnAnSj9vMlSyhW/tM/fsuP3VO7LzbUTGqojKOiXR+48Hi8bNmTx0UcH+OKLwwQHGzhwYDyRkYG8/HImr7ySSWZmb0ymn9+I3W7Jxo02unULQKv98yOXly8/zg03LCQxMZizZyvZsuUuOnRI+tPtUPmLYa2EfWuh481K1t1zqwsLWdyhAy5bFbb7BmKcswitCKDT06NJqr8QY0QinpgmFIfuptJcgVbjxSi1SK+bUwsiqDOkEHdZNJs+foWea0aTYCjiocx72BNzPXU3FFO8P5oF9TqQGBH9K8ZdoGZEo7GE9/1s3VEVjdrIf1Ij3F+sVhcul4fQ0AtjwVLKWp3K4v77lzN79g7uv/+a/6k6EypXJ2VHjrC4c2dcFgtCp+PWLVuIbtPmkjZebJTxHkYaE0ATSngIO2sQBBDHOvTU5+u531N4+ARj9dNxWyoYHPAhzttczEq+jrTon/tqLkfNiEa6hPl+tu6qikZt5I8UjasRm83FZ58dYujQJld9+VOV/w1y16/nu1tuodNLL9Fk3LjfbC+RWPkKDeEE0OPSjQUnYHoXXCKAuU1+5N6xSVzGNXhZakY0Gkp408/WfVXRqI2ooqGiUvvxejxotDXkW8vaBS/2gOj68PgPEOhfvY2aEY0GEt7ws/WAq040akdhXhUVlb88NSYYAClt4f7FEJcOuivRm3b7+br6+CPjNFRUVFSuHE37Kq8/Hcm5Qmn/i6iioaKiolKjqKKhoqKiouI3/9sR4apoqKioqNQoXpT6MP+bqKKhoqKiUqOow1MqKioqKn6jDk+pqKioqPiN2tNQUVFRUfEbtaehoqKiouI3/9uO8L9EGhEhRBGQ/Ts+EgUU/0Hm1BSqjTXH1WCnamPN8Fs2Jksp/UuJ+wsIIb7zfY8/FEsp+/033/dn85cQjd+LEGJnbc8Ho9pYc1wNdqo21gxXg421HTX3lIqKioqK36iioaKioqLiN6poXJ53rrQBfqDaWHNcDXaqNtYMV4ONtRrVp6GioqKi4jdqT0NFRUVFxW9U0bgIIUQ/IcQxIcQJIcTjV9iWd4UQhUKIgxetixBCrBZCHPf9DfetF0KIN3x27xdCtPnlPdeojXWEEOuFEEeEEIeEEJNqm51CCJMQYrsQYp/Pxn/61tcTQmzz2fiZEMLgW2/0LZ/wbU/5o228yFatEGKPEGJZbbRRCJElhDgghNgrhNjpW1drzvVFdoYJIb4QQhz1XZudaqOdVyuqaPgQQmiB2UB/oAkwQgjR5Aqa9D7w0/nbjwNrpZQNgLW+ZVBsbuB73Q3M+ZNsdAMPSykbAx2BCb5jVpvsdAC9pJQtgVZAPyFER+BF4FWfjWXAXb72dwFlUso04FVfuz+LScCRi5Zro409pZStLpq2WpvO9TleB76TUqYDLVGOaW208+pESqm+FL9OJ2DlRctTgClX2KYU4OBFy8eAeN/7eOCY7/3bwIjLtfuT7f0a6Ftb7QQCgd1AB5QAL91Pzz2wEujke6/ztRN/gm1JKDezXsAyQNRCG7OAqJ+sq1XnGggBTv30eNQ2O6/ml9rTuEAicOai5RzfutpErJQyD8D3N8a3/orb7hsiaQ1so5bZ6Rv22QsUAquBTKBcSnkuQdDFdpy30be9Aoj8o20EXgP+gZKDAt931jYbJbBKCLFLCHG3b12tOtdAfaAIeM831DdPCGGuhXZetaiicQFxmXVXy9SyK2q7ECII+BKYLKW0/FrTy6z7w+2UUnqklK1QnubbA41/xY4/3UYhxI1AoZRy18Wrf8WOK3W+u0gp26AM6UwQQnT7lbZXykYd0AaYI6VsDVRzYSjqclzN//dXBFU0LpAD1LloOQk4e4Vs+SUKhBDxAL6/hb71V8x2IYQeRTA+llIurq12Akgpy4ENKP6XMCHEuYSdF9tx3kbf9lCg9A82rQtwkxAiC/gUZYjqtVpmI1LKs76/hcASFAGubec6B8iRUm7zLX+BIiK1zc6rFlU0LrADaOCbsWIAhgNLr7BNP2UpMNr3fjSKD+Hc+jt8M0E6AhXnuuJ/JEIIAcwHjkgpX6mNdgohooUQYb73AUAfFMfoemDIL9h4zvYhwDrpG+z+o5BSTpFSJkkpU1Cuu3VSyttqk41CCLMQIvjce+A64CC16FwDSCnzgTNCiEa+Vb2Bw7XNzquaK+1UqU0vYACQgTLmPe0K2/IJkIdSzSUHZcZMJIqz9Ljvb4SvrUCZ+ZUJHADa/Uk2dkXpyu8H9vpeA2qTnUALYI/PxoPAk7719YHtwAlgEWD0rTf5lk/4ttf/k897D2BZbbPRZ8s+3+vQuf+P2nSuL7K1FbDTd86/AsJro51X60uNCFdRUVFR8Rt1eEpFRUVFxW9U0VBRUVFR8RtVNFRUVFRU/EYVDRUVFRUVv1FFQ0VFRUXFb1TRUPnLIJSsvKeEEBG+5XDfcvKVtk1F5WpBFQ2VvwxSyjMoWUxf8K16AXhHSpl95axSUbm6UOM0VP5S+NKe7ALeBcYBraWUzitrlYrK1YPut5uoqPzvIKV0CSEeBb4DrlMFQ0Xl96EOT6n8FemPkqKl2ZU2REXlakMVDZW/FEKIViiFojoCD57LfKqiouIfqmio/GXwZeWdg1L34zTwEjDjylqlonJ1oYqGyl+JccBpKeVq3/KbQLoQovsVtElF5apCnT2loqKiouI3ak9DRUVFRcVvVNFQUVFRUfEbVTRUVFRUVPxGFQ0VFRUVFb9RRUNFRUVFxW9U0VBRUVFR8RtVNFRUVFRU/EYVDRUVFRUVv/l/40gJ4tlMKKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,600,10.0)\n",
    "nb_samples = 40 # number of samples in our dataset\n",
    "\n",
    "S_1 = norm.pdf(x,loc=200.,scale=100.)\n",
    "S_2 = norm.pdf(x,loc=400,scale=50)\n",
    "S_true = np.vstack((S_1,S_2))\n",
    "print(\"Number of samples:\"+str(nb_samples))\n",
    "print(\"Shape of partial spectra matrix:\"+str(S_true.shape))\n",
    "\n",
    "C_ = np.random.rand(nb_samples) #60 samples with random concentrations between 0 and 1\n",
    "C_true = np.vstack((C_,(1-C_))).T\n",
    "print(\"Shape of concentration matrix:\"+str(C_true.shape))\n",
    "\n",
    "Obs = np.dot(C_true,S_true) + np.random.randn(nb_samples,len(x))*1e-4\n",
    "\n",
    "# norm is a class which, when called, can normalize data into the\n",
    "# [0.0, 1.0] interval.\n",
    "norm_ = matplotlib.colors.Normalize(\n",
    "    vmin=np.min(C_),\n",
    "    vmax=np.max(C_))\n",
    "\n",
    "# choose a colormap\n",
    "c_m = matplotlib.cm.jet\n",
    "\n",
    "# create a ScalarMappable and initialize a data structure\n",
    "s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm_)\n",
    "s_m.set_array([])\n",
    "\n",
    "# plotting spectra\n",
    "# calling the ScalarMappable that was initialised with c_m and norm\n",
    "for i in range(C_.shape[0]):\n",
    "    plt.plot(x,\n",
    "             Obs[i,:].T,\n",
    "             color=s_m.to_rgba(C_[i]))\n",
    "\n",
    "# we plot the colorbar, using again our\n",
    "# ScalarMappable\n",
    "c_bar = plt.colorbar(s_m)\n",
    "c_bar.set_label(r\"C_\")\n",
    "\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create an mlexplorer object called 'explo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "explo = rp.mlexplorer(Obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will explore the data with a Non-Negative Matrix factorisation algorithm, training it with 2 components on 70% of the data. MinMax scaling is performed before training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "explo.algorithm = 'NMF'\n",
    "explo.nb_compo = 2\n",
    "explo.test_size = 0.3\n",
    "explo.scaler = \"MinMax\"\n",
    "explo.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code uses the functions from scikit-learn, if you want a low-level control, you can define everything using the scikit-learn API and manipulating the model.\n",
    "\n",
    "Training and testing data (original and scaled) are embedded into the object, as well as the scaler and model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example of X data (testing):\n",
      "[0.00013143 0.00014004 0.00025803 0.00051724]\n",
      "\n",
      "Scaler :\n",
      "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
      "\n",
      "Model :\n",
      "NMF(alpha=0.0, beta_loss='frobenius', init='nndsvd', l1_ratio=0.0,\n",
      "  max_iter=200, n_components=2, random_state=None, shuffle=False,\n",
      "  solver='cd', tol=0.0001, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(\"Example of X data (testing):\")\n",
    "print(explo.X_test[1,1:5])\n",
    "print(\"\\nScaler :\")\n",
    "print(explo.X_scaler)\n",
    "print(\"\\nModel :\")\n",
    "print(explo.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please directly see the scikit-learn documentation for information about their PCA and NMF algorithms.\n",
    "\n",
    "We can use directly the model to extract the W and H matrix and plot them, following the scikit-learn API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7a4ae075c0>,\n",
       " <matplotlib.lines.Line2D at 0x7f7a491a8470>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W = explo.model.transform(explo.X_train_sc) # mixture array\n",
    "H = explo.X_scaler.inverse_transform(explo.model.components_) # components in the original space\n",
    "plt.plot(x,H.T) # plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can modify hyperparameter and refit the data with the refit function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMF(alpha=0.1, beta_loss='frobenius', init='nndsvd', l1_ratio=0.0,\n",
      "  max_iter=4000, n_components=2, random_state=None, shuffle=False,\n",
      "  solver='cd', tol=1e-12, verbose=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7a4915cb00>,\n",
       " <matplotlib.lines.Line2D at 0x7f7a491908d0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explo.model.tol=1e-12\n",
    "explo.model.n_components=2\n",
    "explo.model.max_iter = 4000\n",
    "explo.model.solver=\"cd\"\n",
    "explo.model.alpha=0.1\n",
    "print(explo.model)\n",
    "explo.refit()\n",
    "W = explo.model.transform(explo.X_train_sc) # mixture array\n",
    "H = explo.X_scaler.inverse_transform(explo.model.components_) # components in the original space\n",
    "plt.plot(x,H.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we try a PCA..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7a490efb70>,\n",
       " <matplotlib.lines.Line2D at 0x7f7a490efcf8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explo.algorithm = 'PCA'\n",
    "explo.nb_compo = 2\n",
    "explo.fit()\n",
    "\n",
    "H_PCA = explo.X_scaler.inverse_transform(explo.model.components_)\n",
    "plt.plot(x,H_PCA.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
